Mercurial > repos > iuc > mageck_mle
changeset 4:b34c9d6373e0 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mageck commit 4478aabdcb10e4787450b1b23944defa7dc38ffe
| author | iuc |
|---|---|
| date | Mon, 04 Jun 2018 10:58:04 -0400 |
| parents | 5dfc0e462f2a |
| children | b36c85e37a48 |
| files | mageck_macros.xml mageck_mle.xml test-data/in.mle.sgrnaeff test-data/out.count.bam.txt test-data/out.count.fastq.txt test-data/out.count.txt test-data/out.count_multi.txt test-data/out.test.R test-data/out.test.log.txt test-data/out.test.report.pdf test-data/out.test.sgrna_summary.txt test-data/output.count_normalized.txt test-data/output_summary.Rnw |
| diffstat | 13 files changed, 156 insertions(+), 3118 deletions(-) [+] |
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--- a/mageck_macros.xml Thu Apr 19 05:34:53 2018 -0400 +++ b/mageck_macros.xml Mon Jun 04 10:58:04 2018 -0400 @@ -6,7 +6,6 @@ <xml name="requirements"> <requirements> <requirement type="package" version="@VERSION@">mageck</requirement> - <requirement type="package" version="1.14.2">numpy</requirement> <requirement type="package" version="3.0.1">r-gplots</requirement> <requirement type="package" version="1.8_2">r-xtable</requirement> <yield/> @@ -15,7 +14,7 @@ <xml name="version"> <version_command><![CDATA[ - echo $(mageck -v )", numpy version" $([python -c "import numpy; numpy.version.version"])", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ") + echo $(mageck -v )", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ") ]]></version_command> </xml>
--- a/mageck_mle.xml Thu Apr 19 05:34:53 2018 -0400 +++ b/mageck_mle.xml Mon Jun 04 10:58:04 2018 -0400 @@ -46,10 +46,12 @@ --adjust-method $adv.adjust_method #end if -#if $adv.sgrnaeff_file: - --sgrna-efficiency $adv.sgrnaeff_file - --sgrna-eff-name-column $adv.sgrnaid_col - --sgrna-eff-score-column $adv.sgrnaeff_col +#if $adv.sgrnaeff.sgrnaeff_file_select == "yes": + #set $nindex = int(str($adv.sgrnaeff.sgrnaeff_name_col)) - 1 + #set $sindex = int(str($adv.sgrnaeff.sgrnaeff_score_col)) - 1 + --sgrna-efficiency $adv.sgrnaeff.sgrnaeff_file + --sgrna-eff-name-column $nindex + --sgrna-eff-score-column $sindex #end if $adv.remove_outliers @@ -98,9 +100,18 @@ <option value="holm">Holm</option> <option value="pounds">Pounds</option> </param> - <param name="sgrnaeff_file" argument="--sgrna-efficiency" type="data" format="tabular" optional="true" label="sgRNA efficiency file" help="An optional file of sgRNA efficiency prediction. The efficiency prediction will be used as an initial guess of the probability an sgRNA is efficient. Must contain at least two columns, one containing sgRNA ID, the other containing sgRNA efficiency prediction" /> - <param name="sgrnaeff_name_col" argument="--sgrna-eff-score-column" type="data_column" data_ref="sgrnaeff_file" value="1" optional="true" label="sgRNA score column" help="The sgRNA efficiency prediction column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 1 (the second column)." /> - <param name="sgrnaeff_score_col" argument="--sgrna-eff-name-column" type="data_column" data_ref="sgrnaeff_file" value="0" optional="true" label="sgRNA ID column" help="The sgRNA ID column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 0 (the first column)" /> + <conditional name="sgrnaeff"> + <param name="sgrnaeff_file_select" type="select" label="Incorporate sgRNA efficiency" help="Optionally sgRNA efficiency information can be incorporated into the analysis. See the MAGeCK website here for more information: https://sourceforge.net/p/mageck/wiki/Home/#tutorial-3-include-the-sgrna-efficiency-into-mle-calculation"> + <option value="yes">Yes</option> + <option value="no" selected="True">No</option> + </param> + <when value="yes"> + <param name="sgrnaeff_file" argument="--sgrna-efficiency" type="data" format="tabular" label="sgRNA efficiency file" help="A file of sgRNA efficiency prediction from the SSC program. The efficiency prediction will be used as an initial guess of the probability an sgRNA is efficient. Must contain at least two columns, one containing sgRNA ID, the other containing sgRNA efficiency prediction." /> + <param name="sgrnaeff_name_col" argument="--sgrna-eff-name-column" type="data_column" data_ref="sgrnaeff_file" value="1" label="sgRNA ID column" help="The sgRNA ID column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 1 (the first column)" /> + <param name="sgrnaeff_score_col" argument="--sgrna-eff-score-column" type="data_column" data_ref="sgrnaeff_file" value="2" label="sgRNA score column" help="The sgRNA efficiency prediction column in sgRNA efficiency prediction file (specified by the --sgrna-efficiency option). Default is 2 (the second column)." /> + </when> + <when value="no"/> + </conditional> <param name="update_eff" argument="--update-efficiency" type="boolean" truevalue="--update-efficiency" falsevalue="" checked="false" optional="true" label="Update efficiency" help="Iteratively update sgRNA efficiency during EM iteration" /> <param name="out_log" type="boolean" truevalue="True" falsevalue="" checked="false" @@ -119,10 +130,40 @@ <param name="count_table" value="demo/demo1/sample.txt" ftype="tabular" /> <param name="design_matrix" ftype="tabular" value="in.mle.design_matrix.txt" /> <param name="out_log" value="True"/> - <output name="gene_summary" file="out.mle.gene_summary.txt" compare="sim_size"/> - <output name="sgrna_summary" file="out.mle.sgrna_summary.txt"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*beta.*z.*p-value.*fdr.*wald-p-value.*wald-fdr.*beta.*p-value.*fdr.*wald-p-value.*wald-fdr" /> + <has_text_matching expression="A1CF.*10.*0.05018.*0.3479.*0.7278.*0.8665.*0.0927.*0.6435.*0.5198.*0.8170"/> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*eff" /> + <has_text_matching expression="ADNP2.*ADNP2_m77891006.*1"/> + </assert_contents> + </output> <output name="log" file="out.mle.log.txt" compare="sim_size"/> </test> + <test><!-- Ensure sgRNA efficiency file works --> + <param name="count_table" value="demo/demo1/sample.txt" ftype="tabular" /> + <param name="design_matrix" ftype="tabular" value="in.mle.design_matrix.txt" /> + <param name="sgrnaeff_file_select" value="yes"/> + <param name="sgrnaeff_file" value="in.mle.sgrnaeff"/> + <param name="sgrnaeff_name_col" value="2"/> + <param name="sgrnaeff_score_col" value="4"/> + <output name="gene_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*beta.*z.*p-value.*fdr.*wald-p-value.*wald-fdr.*beta.*p-value.*fdr.*wald-p-value.*wald-fdr" /> + <has_text_matching expression="A1CF.*10.*0.05018.*0.3479.*0.7278.*0.8665.*0.0927.*0.6435.*0.5198.*0.8252"/> + </assert_contents> + </output> + <output name="sgrna_summary"> + <assert_contents> + <has_text_matching expression="Gene.*sgRNA.*eff" /> + <has_text_matching expression="ADNP2.*ADNP2_m77891006.*0.646"/> + </assert_contents> + </output> + </test> </tests> <help><![CDATA[
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/in.mle.sgrnaeff Mon Jun 04 10:58:04 2018 -0400 @@ -0,0 +1,100 @@ +CATGGCCATGGGCACCCGCC INO80B_m74682554 INO80B -0.005239 +AGGAGCTGCACCGCCACGCC NHS_p17705966 NHS -0.018779 +AACCACCAGCTGGTCCCGCC MED14_m40594623 MED14 -0.018132 +CAGCAACAGCCACCGCCATT MX2_p42748920 MX2 -0.012714 +CAGGCCTCACCTGCACCGCC SH3GLB2_p131790360 SH3GLB2 -0.072219 +ATGATTCTTCACCTGCCGCT RECQL_m21644465 RECQL -0.106925 +CTGCACCAGCCATATCGCGC CCDC170_p151815274 CCDC170 -0.015925 +ACGTCTCCGCCAGCCACTCC MEIS2_p37388538 MEIS2 -0.061877 +AAGGTGGCATGGACCCGCCA NTRK2_p87285677 NTRK2 0.062009 +GCTTCAAAGCACCCGCCGCC AIFM1_p129299590 AIFM1 -0.116716 +AGGACGAGGGCCGCCGTGAC PIN1_m9949250 PIN1 0.084799 +GGATTTCAGAACCGCCTGTG SNTG1_p51306800 SNTG1 -0.002402 +CGCTGCCGTTCCCGCCGCTC PHLPP1_m60383095 PHLPP1 -0.065516 +TCTTCATCACGCCGCCCGCC DIS3_p73355915 DIS3 -0.178900 +TAATCCTTGCCCGCCATGTC APLF_p68694848 APLF -0.125824 +CGAGGAAGGCGAGAACCGCC HNRPLL_m38829641 HNRPLL 0.208905 +TAATGATAAGCAGACCCGCC SCRN1_m29994913 SCRN1 -0.042915 +GGAGCCGCCGCCGCCATATC RNF11_p51702529 RNF11 -0.022538 +AAGAGCGCACCTGCCACTGG HIST3H3_m228612925 HIST3H3 0.041563 +ACAGATGTCCAGCAACCGCC SETD1B_p122243019 SETD1B -0.022566 +GAACAAAGAACCGCCGGCGC TMEM165_p56262481 TMEM165 -0.068287 +GGAACAGAAGCTGTCCCGCC MAP2K7_p7968846 MAP2K7 -0.001348 +ACGTTCCCGCCGCCGCCGTT TAF5_m105127878 TAF5 -0.075037 +TTATGCCCTGAACACCCGCC FAM184B_m17711242 FAM184B -0.060696 +TCTCCTCCACCCGCCGGGTC ZW10_p113644288 ZW10 -0.132037 +AAAGTTGCAGCCGCCACTGC SNAPC3_m15422944 SNAPC3 -0.064822 +GAGCTGAGCCTGCCACGCCG MAST3_p18218399 MAST3 0.097420 +TAGTCTTGTCCCTACCCGCC DDX31_m135545488 DDX31 -0.182423 +GACGGCGTGCAGCTCCCGCC EIF4EBP1_p37888163 EIF4EBP1 0.027745 +CAGGACTACCGCCATATCCC ZNF35_m44694064 ZNF35 -0.065333 +CGGCCGCTACCGCCGCTACC DSTYK_m205180551 DSTYK -0.121018 +GACCTTTGGAGTTCATCAAA TCF12_p57524508 TCF12 0.075702 +TGGGAAGGCGTCCGACCGCC C21orf33_m45553648 C21orf33 -0.011841 +CCGGGACCGCCACTGCCGAG SYN1_m47478971 SYN1 0.168301 +CTCGGACGAGCGGCTGGGCC TXNRD3_p126373683 TXNRD3 -0.003583 +TCTCATTGACCGGACCCGCC SNRNP200_m96970554 SNRNP200 -0.101923 +GGAGCGCACCCGCCGCGGAA C9orf69_p139008710 C9orf69 0.127616 +TCGCTTCCGCCGCCACTGCC NPAS1_m47524322 NPAS1 -0.003985 +GGATGCGACCGCCACTATCG SMARCA1_m128657283 SMARCA1 0.063867 +CCCAAATGCGCCCGCCTGCA WDR77_m111991712 WDR77 0.023250 +CTCACGAGCCGAGCCTCTCG PNCK_p152938458 PNCK 0.124364 +CAACGAAGACGCTCCCGCCT SENP3_m7466474 SENP3 0.024559 +GCCGCCGAAACCGCCACGGA RFWD2_p176175980 RFWD2 0.003107 +GCTCACCTTCACCGCCGCTC C9orf9_p135759399 C9orf9 -0.134392 +TGAAACCCTGGCCCGCCAGT C4orf22_p81283909 C4orf22 -0.016684 +GGGCTGCTCCTGCTTCCTCC ZNF827_p146859532 ZNF827 -0.094275 +GTGGTGCCACCCGCCGTGGC EPM2A_m146056593 EPM2A -0.112144 +GTGTATCCAGTGCCTGCTCT TMEM175_m941547 TMEM175 -0.249848 +GCAGCGCCAGTCCCGCCAGC DUSP23_m159750934 DUSP23 -0.134388 +AGTGGCTGCTCCCGCCATAC YLPM1_m75230212 YLPM1 -0.007259 +AGCACCACCAGCTGTTGCTC RAB1A_p65315689 RAB1A -0.032893 +AGTGGTTCCCGCCGCAGGAC GSG2_p3627326 GSG2 0.033727 +CAGAACCCGCCACTTGTCCA RPL10L_p47120303 RPL10L 0.026564 +GACCATGAACCGCAGCCGCC PWP1_p108079672 PWP1 -0.090761 +AAGGAGGATAGAGGCCCGCC KIAA1755_p36874470 KIAA1755 0.005710 +TCTCCAATACCTGCCGATTC JAZF1_m28220148 JAZF1 -0.153656 +CGTTCACCCGCCGGGCCTTT PAXBP1_p34143951 PAXBP1 -0.074149 +GGTAGCCAGGGCACCCGCCA BMPR2_m203242221 BMPR2 0.152337 +GCCTCCACCGCCCGCCGCTT UXT_p47518291 UXT -0.042766 +ACTCTCACCGCCGTAGGTGC POU2AF1_p111229504 POU2AF1 -0.026811 +CTGGACGACCGCCACGACAG NFKBIA_m35873755 NFKBIA 0.076979 +CTGAATCCCGCCACACTCTC LPIN1_p11905730 LPIN1 -0.025123 +TACCGGAGCCGCGACCGCCT ZNF746_p149194600 ZNF746 0.173225 +TTTATCTGCATTTCCATGAC SYNPO2_p119810206 SYNPO2 -0.140214 +TCACTCCTGAACAGACTTCT LTA4H_m96421257 LTA4H -0.030193 +GATGGTCGCCGCCTGCCGCT TMEM87B_p112813167 TMEM87B 0.036418 +CTGGATGGAGCCGCCGCTCC OCRL_p128674412 OCRL -0.150457 +AGTGGACATCCGCCATAACA RPL18_m49121112 RPL18 0.056941 +GACAAGGCGAAACCGCCGCC PSMD3_p38137259 PSMD3 0.030238 +AGCCACACCGAGAACCGCCG WBP2NL_p42394837 WBP2NL 0.131323 +CGGCCTCCAGCCGCCACTTG LUZP1_m23420706 LUZP1 -0.002771 +ATTCTCTTTGGAGCCGTGAG STRADB_p202340406 STRADB -0.001874 +GCCTCTTCTCCGCGCTCTCG FOXL2_p138665341 FOXL2 -0.150694 +CTGCACCCAACCGCCGGCAC ADRA1A_m26722428 ADRA1A -0.096324 +TCACCCAGCCATACCAGCCG RBBP9_p18477729 RBBP9 0.064510 +AGTCCTCCCGCCGCTGCAGC IMP3_p75932376 IMP3 -0.091312 +TGGTGTCTCATCTCCTTGCC RABGAP1_m125719426 RABGAP1 -0.201102 +TTTAGGAGCTTCTCCAAATT RARS_p167915662 RARS -0.079412 +ACAGGCCCGCCACGTCCGTC RPRM_p154335036 RPRM 0.003556 +GGACATGAAGGAGTCCCGCC OBFC1_m105677228 OBFC1 0.039483 +GAGCTGCGGGACCCGCCACC TINF2_p24711504 TINF2 -0.010376 +TTCTTCCAGAGAGAACTCTA ZNF565_p36686010 ZNF565 0.131393 +CACATTCTCCACCCGCCGAG CCDC78_p776304 CCDC78 0.076491 +TAGGCGCCCGCCGCTCTTCC YWHAB_p43530334 YWHAB -0.008675 +CGCTCCCGCCGCTGCTTCCT CRX_m48339507 CRX -0.050130 +AGCGGCACCTACACCCGCCA EXOSC1_m99205546 EXOSC1 0.090355 +CGCGGTGGGCAAGACGAGCC RHOU_p228871662 RHOU 0.144042 +TTCTCAAGAAATTCACCGCC CHMP1B_m11851692 CHMP1B -0.112913 +CACCGCCACCGCCACGACCA U2AF1_p44513288 U2AF1 0.080210 +TCCCAACACCCGCCAAGAGA NET1_p5494387 NET1 0.089082 +TCTGAGCTCCAGGTGCTTCT PIAS3_p145578085 PIAS3 -0.027557 +TTCGCCCGCCGGCTCCTGCG CMPK2_m7005801 CMPK2 0.105524 +ACCAGCCAAGATTGCCCGCC SATB1_m18462362 SATB1 -0.070132 +GACACACCTCGCCCGCCTCC FOXA3_m46367770 FOXA3 -0.054864 +AAATTCCCAGGAGAAATATA ZNF627_p11725680 ZNF627 0.090453 +GTCACGGCCGCCCGCCGACA MLLT4_m168227813 MLLT4 0.040685 +AACTGCCTGCACCGCCTCTA FAM120A_p96214350 FAM120A 0.010379 +TTATGAAAGTATTTCTCTCC ADNP2_m77891006 ADNP2 -0.159761 +CGCACCCTCACCGCCGGCCT CD3EAP_m45909967 CD3EAP -0.028605 +GTGGACCCTCGTGAGCGACC HSF1_p145515504 HSF1 -0.051256
--- a/test-data/out.count.bam.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.bam.txt Mon Jun 04 10:58:04 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_bam +sgRNA Gene test1.bam s_10007 CCNA1 0 s_10008 CCNA1 0 s_10027 CCNC 0
--- a/test-data/out.count.fastq.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.fastq.txt Mon Jun 04 10:58:04 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 1 s_24835 HCFC1R1 1 s_14784 CYP4B1 4
--- a/test-data/out.count.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count.txt Mon Jun 04 10:58:04 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 1 s_24835 HCFC1R1 1 s_14784 CYP4B1 4
--- a/test-data/out.count_multi.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/out.count_multi.txt Mon Jun 04 10:58:04 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz test2_fastq_gz +sgRNA Gene test1.fastq.gz test2.fastq.gz s_47512 RNF111 1 0 s_24835 HCFC1R1 1 0 s_14784 CYP4B1 4 0
--- a/test-data/out.test.R Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,930 +0,0 @@ -pdf(file='output.pdf',width=4.5,height=4.5); -gstable=read.table('output.gene_summary.txt',header=T) -# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=3 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1") -# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285)) -targetgene="ACIN1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424)) -targetgene="ACTR8" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344)) -targetgene="AHCY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021)) -targetgene="ACLY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173)) -targetgene="AATF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033)) -targetgene="AGBL5" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991)) -targetgene="AHCTF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427)) -targetgene="ABT1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338)) -targetgene="ADIRF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927)) -targetgene="ABCF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=9 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP") -# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192)) -targetgene="ACRC" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882)) -targetgene="AGAP3" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471)) -targetgene="ADCK4" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885)) -targetgene="AHRR" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979)) -targetgene="ADRBK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076)) -targetgene="ADK" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816)) -targetgene="ADCK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512)) -targetgene="ADARB2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832)) -targetgene="ACSS2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625)) -targetgene="ADNP" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -dev.off() -Sweave("output_summary.Rnw"); -library(tools); - -texi2dvi("output_summary.tex",pdf=TRUE); -
--- a/test-data/out.test.log.txt Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,109 +0,0 @@ -INFO @ Mon, 26 Mar 2018 08:37:53: Parameters: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/bin/mageck test -k /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat -t HL60_final,KBM7_final -c HL60_initial,KBM7_initial -n output --normcounts-to-file --pdf-report --norm-method median --adjust-method fdr --sort-criteria neg --remove-zero both --gene-lfc-method median -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to MAGeCK v0.5.7. Command: test -INFO @ Mon, 26 Mar 2018 08:37:53: Loading count table from /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat -INFO @ Mon, 26 Mar 2018 08:37:53: Processing 1 lines.. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Parsing error in line 1 (usually the header line). Skip this line. -INFO @ Mon, 26 Mar 2018 08:37:53: Loaded 999 records. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.RTemplate. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template_indvgene.RTemplate. -INFO @ Mon, 26 Mar 2018 08:37:53: Loading Rnw template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.Rnw. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Setting up the visualization module... -DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_final,KBM7_final -DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 2 3 -INFO @ Mon, 26 Mar 2018 08:37:53: Treatment samples:HL60_final,KBM7_final -INFO @ Mon, 26 Mar 2018 08:37:53: Treatment sample index:2,3 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_initial,KBM7_initial -DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 0 1 -INFO @ Mon, 26 Mar 2018 08:37:53: Control samples:HL60_initial,KBM7_initial -INFO @ Mon, 26 Mar 2018 08:37:53: Control sample index:0,1 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Initial (total) size factor: 1.6666455325878438 2.027372749328462 0.7198064117880387 0.6589869375844738 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Median factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 -INFO @ Mon, 26 Mar 2018 08:37:53: Final size factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 -INFO @ Mon, 26 Mar 2018 08:37:53: Writing normalized read counts to output.normalized.txt -DEBUG @ Mon, 26 Mar 2018 08:37:53: Adjusted model: 1.1175084644498339 3.4299551007579927 -INFO @ Mon, 26 Mar 2018 08:37:53: Raw variance calculation: 0.5 for control, 0.5 for treatment -INFO @ Mon, 26 Mar 2018 08:37:53: Adjusted variance calculation: 0.3333333333333333 for raw variance, 0.6666666666666667 for modeling -INFO @ Mon, 26 Mar 2018 08:37:53: Use qnorm to reversely calculate sgRNA scores ... -DEBUG @ Mon, 26 Mar 2018 08:37:53: lower test FDR cutoff: 0.3283283283283283 -DEBUG @ Mon, 26 Mar 2018 08:37:53: higher test FDR cutoff: 0.34534534534534533 -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.plow.txt -o output.gene.low.txt -p 0.3283283283283283 --skip-gene NA --skip-gene na -INFO @ Mon, 26 Mar 2018 08:37:53: Command message: -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to RRA v 0.5.7. -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene NA for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene na for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Reading input file... -INFO @ Mon, 26 Mar 2018 08:37:53: Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 -INFO @ Mon, 26 Mar 2018 08:37:53: Computing lo-values for each group... -INFO @ Mon, 26 Mar 2018 08:37:53: Computing false discovery rate... -INFO @ Mon, 26 Mar 2018 08:37:53: Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 9 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 10 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Number of genes under FDR adjustment: 100 -INFO @ Mon, 26 Mar 2018 08:37:53: Saving to output file... -INFO @ Mon, 26 Mar 2018 08:37:53: RRA completed. -INFO @ Mon, 26 Mar 2018 08:37:53: -INFO @ Mon, 26 Mar 2018 08:37:53: End command message. -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.phigh.txt -o output.gene.high.txt -p 0.34534534534534533 --skip-gene NA --skip-gene na -INFO @ Mon, 26 Mar 2018 08:37:53: Command message: -INFO @ Mon, 26 Mar 2018 08:37:53: Welcome to RRA v 0.5.7. -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene NA for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Skipping gene na for permutation ... -INFO @ Mon, 26 Mar 2018 08:37:53: Reading input file... -INFO @ Mon, 26 Mar 2018 08:37:53: Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 -INFO @ Mon, 26 Mar 2018 08:37:53: Computing lo-values for each group... -INFO @ Mon, 26 Mar 2018 08:37:53: Computing false discovery rate... -INFO @ Mon, 26 Mar 2018 08:37:53: Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 9 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Permuting genes with 10 sgRNAs... -INFO @ Mon, 26 Mar 2018 08:37:53: Number of genes under FDR adjustment: 100 -INFO @ Mon, 26 Mar 2018 08:37:53: Saving to output file... -INFO @ Mon, 26 Mar 2018 08:37:53: RRA completed. -INFO @ Mon, 26 Mar 2018 08:37:53: -INFO @ Mon, 26 Mar 2018 08:37:53: End command message. -DEBUG @ Mon, 26 Mar 2018 08:37:53: Sorting the merged items by negative selection... -INFO @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.low.txt: ACIN1,ACTR8,AHCY,ACLY,AATF,AGBL5,AHCTF1,ABT1,ADIRF,ABCF1 -DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:3 -INFO @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.high.txt: ACRC,AGAP3,ADCK4,AHRR,ADRBK1,ADK,ADCK1,ADARB2,ACSS2,ADNP -DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:9 -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.plow.txt -INFO @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.phigh.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.low.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.high.txt -INFO @ Mon, 26 Mar 2018 08:37:54: Running command: cd ./; Rscript output.R -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: null device -INFO @ Mon, 26 Mar 2018 08:37:59: 1 -INFO @ Mon, 26 Mar 2018 08:37:59: Writing to file output_summary.tex -INFO @ Mon, 26 Mar 2018 08:37:59: Processing code chunks with options ... -INFO @ Mon, 26 Mar 2018 08:37:59: 1 : keep.source term verbatim (label = funcdef, output_summary.Rnw:27) -INFO @ Mon, 26 Mar 2018 08:37:59: 2 : keep.source term tex (label = tab1, output_summary.Rnw:37) -INFO @ Mon, 26 Mar 2018 08:37:59: 3 : keep.source term verbatim (output_summary.Rnw:77) -INFO @ Mon, 26 Mar 2018 08:37:59: 4 : keep.source term verbatim pdf (output_summary.Rnw:83) -INFO @ Mon, 26 Mar 2018 08:37:59: 5 : keep.source term verbatim pdf (output_summary.Rnw:201) -INFO @ Mon, 26 Mar 2018 08:37:59: 6 : keep.source term verbatim pdf (output_summary.Rnw:345) -INFO @ Mon, 26 Mar 2018 08:37:59: 7 : keep.source term verbatim pdf (output_summary.Rnw:489) -INFO @ Mon, 26 Mar 2018 08:37:59: 8 : keep.source term verbatim (output_summary.Rnw:567) -INFO @ Mon, 26 Mar 2018 08:37:59: 9 : keep.source term verbatim pdf (output_summary.Rnw:573) -INFO @ Mon, 26 Mar 2018 08:37:59: 10 : keep.source term verbatim pdf (output_summary.Rnw:691) -INFO @ Mon, 26 Mar 2018 08:37:59: 11 : keep.source term verbatim pdf (output_summary.Rnw:835) -INFO @ Mon, 26 Mar 2018 08:37:59: 12 : keep.source term verbatim pdf (output_summary.Rnw:979) -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: You can now run (pdf)latex on ‘output_summary.tex’ -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary-*.pdf -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.aux -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.tex -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message. -INFO @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.toc -INFO @ Mon, 26 Mar 2018 08:37:59: Command message: -INFO @ Mon, 26 Mar 2018 08:37:59: -INFO @ Mon, 26 Mar 2018 08:37:59: End command message.
--- a/test-data/out.test.sgrna_summary.txt Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1000 +0,0 @@ -sgrna Gene control_count treatment_count control_mean treat_mean LFC control_var adj_var score p.low p.high p.twosided FDR high_in_treatment -AHRR_p344008 AHRR 251.35/221.99 1564.6/1502.5 236.67 1533.6 2.6908 1178.2 5085.6 18.186 1 3.3319e-74 6.6638e-74 6.6571e-71 True -ACAD9_m128598565 ACAD9 739.35/925.27 2528/2050.7 832.31 2289.3 1.4586 65590 35597 7.7226 1 5.7002e-15 1.14e-14 5.6945e-12 True -ACTR8_m53916081 ACTR8 1925.5/2054.8 194.94/235.17 1990.2 215.06 -3.2041 4580.2 54357 7.6137 1.3311e-14 1 2.6622e-14 7.8608e-12 False -ACRC_m70814198 ACRC 76.433/90.573 447.3/357.04 83.503 402.17 2.2543 2086.7 1760.4 7.5952 1 1.5737e-14 3.1475e-14 7.8608e-12 True -ABCC1_p16101710 ABCC1 52.915/26.639 203.78/224.3 39.777 214.04 2.3987 277.85 700.66 6.5833 1 2.465e-11 4.93e-11 9.8502e-09 True -ACTR8_m53916067 ACTR8 1267/1156.1 251.09/42.341 1211.6 146.72 -3.0372 13968 31286 6.021 8.6646e-10 1 1.7329e-09 2.3419e-07 False -AAK1_m69870125 AAK1 402.74/621.58 1149.5/1202.2 512.16 1175.8 1.1974 12666 12223 6.0027 1 9.7009e-10 1.9402e-09 2.3419e-07 True -ADCK1_p78285331 ADCK1 798.14/768.99 1478.8/1756 783.56 1617.4 1.0446 19425 19317 5.9996 1 9.8882e-10 1.9776e-09 2.3419e-07 True -AHCY_m32883247 AHCY 1142.1/1099.3 112.93/100.7 1120.7 106.82 -3.379 494.86 28685 5.9891 1.0549e-09 1 2.1098e-09 2.3419e-07 False -AHCTF1_m247070995 AHCTF1 1437.5/1095.8 320.49/161.36 1266.6 240.92 -2.3895 35534 33759 5.5826 1.1847e-08 1 2.3695e-08 2.3671e-06 False -AHCY_p32883309 AHCY 1053.9/882.65 106.62/105.85 968.27 106.24 -3.1761 7331.9 24378 5.524 1.6565e-08 1 3.3129e-08 3.0087e-06 False -ADCY1_p45614315 ADCY1 2.9397/72.814 210.72/187.1 37.877 198.91 2.3624 1360 895.72 5.3806 1 4.1374e-08 8.2747e-08 6.6138e-06 True -ACTN4_p39138476 ACTN4 449.78/475.95 1056.7/977.86 462.87 1017.3 1.1344 1726.9 10724 5.3539 1 4.3033e-08 8.6065e-08 6.6138e-06 True -ADAM12_m128076658 ADAM12 768.74/767.21 1432.1/1562.6 767.98 1497.4 0.96239 4258.6 18836 5.3145 1 5.3464e-08 1.0693e-07 7.63e-06 True -ABT1_m26597388 ABT1 1743.3/1980.2 837.19/281.51 1861.7 559.35 -1.733 91226 64053 5.1459 1.3309e-07 1 2.6618e-07 1.7727e-05 False -ACRC_p70814182 ACRC 682.02/822.26 1572.2/1333.8 752.14 1453 0.949 19128 18646 5.1324 1 1.4307e-07 2.8614e-07 1.7866e-05 True -ACBD6_p180471256 ACBD6 107.3/285.93 553.29/687.76 196.61 620.53 1.6531 12498 6924.5 5.0942 1 1.7666e-07 3.5333e-07 2.0382e-05 True -ACRC_p70811990 ACRC 495.35/605.6 1159.6/1617.6 550.47 1388.6 1.3333 55476 27162 5.0853 1 1.8362e-07 3.6724e-07 2.0382e-05 True -ADRA1B_m159344001 ADRA1B 329.25/309.01 881.98/676.32 319.13 779.15 1.2851 10677 8286.5 5.0535 1 2.1699e-07 4.3398e-07 2.2818e-05 True -AHCY_p32883238 AHCY 61.735/218.44 614.49/452.6 140.09 533.54 1.9217 12691 6123 5.0282 1 2.5699e-07 5.1398e-07 2.5673e-05 True -ACTR5_m37377141 ACTR5 865.75/935.92 1595.5/1695.4 900.84 1645.4 0.86841 3723.6 22496 4.9645 1 3.4447e-07 6.8893e-07 3.2774e-05 True -ADARB2_m1779330 ADARB2 135.23/118.99 351.41/399.95 127.11 375.68 1.556 655.19 2547.9 4.9245 1 4.2536e-07 8.5071e-07 3.863e-05 True -ADRB1_m115804012 ADRB1 2166.6/1942.9 1093.3/705.5 2054.7 899.42 -1.191 50114 56324 4.8681 5.6346e-07 1 1.1269e-06 4.8948e-05 False -AHCTF1_m247070906 AHCTF1 1078.9/1609 348.88/193.97 1343.9 271.43 -2.3036 76257 48827 4.8539 6.0526e-07 1 1.2105e-06 5.0388e-05 False -ACIN1_m23538803 ACIN1 2432.6/2122.3 1067.5/155.63 2277.4 611.55 -1.8952 2.3194e+05 1.1942e+05 4.8207 7.1537e-07 1 1.4307e-06 5.7173e-05 False -ACIN1_m23538698 ACIN1 3424.8/3818.3 1992.3/690.05 3621.5 1341.2 -1.4324 4.627e+05 2.2481e+05 4.8094 7.5677e-07 1 1.5135e-06 5.8155e-05 False -ABCF1_p30545878 ABCF1 2012.3/1989.1 1064.3/432 2000.7 748.15 -1.4179 1.0009e+05 69814 4.7403 1.0669e-06 1 2.1338e-06 7.8951e-05 False -ABCF1_p30539251 ABCF1 1127.4/1198.8 371.59/370.2 1163.1 370.9 -1.6462 1274 29895 4.5817 2.3062e-06 1 4.6124e-06 0.00016456 False -ACTR1A_m104250365 ACTR1A 402.74/161.61 711.64/716.37 282.18 714.01 1.3362 14542 8970.1 4.5595 1 2.5679e-06 5.1359e-06 0.00017692 True -ADRBK1_m67034181 ADRBK1 371.88/877.32 2395.5/1564.9 624.6 1980.2 1.6631 2.3633e+05 88755 4.5503 1 2.728e-06 5.4561e-06 0.00018042 True -AGL_m100327135 AGL 221.95/118.99 418.91/461.75 170.47 440.33 1.3639 3109.1 3531 4.5414 1 2.7993e-06 5.5987e-06 0.00018042 True -AHCY_p32883253 AHCY 651.15/879.09 237.21/18.882 765.12 128.05 -2.5697 24907 20808 4.4189 4.9594e-06 1 9.9187e-06 0.00030965 False -AGTPBP1_m88296213 AGTPBP1 780.5/873.77 1431.5/1476.2 827.13 1453.9 0.81294 2675 20458 4.3817 0.99999 5.8866e-06 1.1773e-05 0.00035641 True -ADK_p76153940 ADK 804.02/472.4 1336.2/1203.3 638.21 1269.8 0.99133 31910 20857 4.373 0.99999 6.1269e-06 1.2254e-05 0.00036004 True -ACD_m67694254 ACD 1174.4/934.15 1644.1/1830.4 1054.3 1737.3 0.72 23111 26800 4.1719 0.99998 1.5104e-05 3.0208e-05 0.00086223 True -ACTR1A_p104262383 ACTR1A 1240.6/836.47 215.76/358.76 1038.5 287.26 -1.8505 45936 32881 4.1431 1.7134e-05 0.99998 3.4269e-05 0.00094812 False -AAK1_p69870105 AAK1 279.28/289.48 581.68/639.7 284.38 610.69 1.0999 867.56 6237.8 4.1316 0.99998 1.8016e-05 3.6033e-05 0.00094812 True -ACTR8_m53916096 ACTR8 1719.7/1007 356.45/222.01 1363.4 289.23 -2.233 1.3154e+05 67630 4.1313 1.8032e-05 0.99998 3.6065e-05 0.00094812 False -ACSS2_p33500893 ACSS2 94.072/447.54 711.64/927.51 270.81 819.57 1.5941 42884 18233 4.0641 0.99998 2.4667e-05 4.9333e-05 0.0012637 True -ACHE_p100491821 ACHE 864.28/1113.5 382.32/331.29 988.9 356.81 -1.4681 16180 24957 4.0012 3.1512e-05 0.99997 6.3025e-05 0.001574 False -ACSL6_m131326651 ACSL6 630.57/692.62 1066.8/1423.6 661.6 1245.2 0.91134 32780 21565 3.9742 0.99996 3.5303e-05 7.0606e-05 0.0017204 True -ADCK4_m41220474 ADCK4 1978.4/1751.1 2659.2/2851.2 1864.8 2755.2 0.56291 22137 50558 3.9601 0.99996 3.7465e-05 7.4931e-05 0.0017823 True -ACLY_p40070033 ACLY 1594.8/1108.2 605.65/127.6 1351.5 366.63 -1.8793 1.1633e+05 62333 3.945 3.9906e-05 0.99996 7.9812e-05 0.0018542 False -ACTRT3_m169487204 ACTRT3 377.76/277.05 599.35/716.94 327.4 658.14 1.0051 5992.9 7296 3.8721 0.99995 5.3953e-05 0.00010791 0.00245 True -ADRB1_m115804005 ADRB1 1346.4/1584.1 791.77/633.98 1465.3 712.87 -1.0384 20355 38658 3.8267 6.4926e-05 0.99994 0.00012985 0.0028827 False -ABCF1_p30545181 ABCF1 1164.1/1204.1 1687/2089 1184.1 1888 0.67261 40807 33934 3.8212 0.99993 6.6413e-05 0.00013283 0.0028846 True -ABTB1_m127395813 ABTB1 492.41/353.41 863.06/731.82 422.91 797.44 0.91342 9135.6 9699.2 3.8029 0.99993 7.1501e-05 0.000143 0.0030341 True -ACLY_m40070008 ACLY 327.78/454.64 51.733/24.032 391.21 37.882 -3.3344 4215.3 8894 3.7981 7.2891e-05 0.99993 0.00014578 0.0030341 False -AGAP3_p150783818 AGAP3 388.05/509.7 933.09/750.13 448.87 841.61 0.90535 12068 10932 3.7563 0.99991 8.624e-05 0.00017248 0.003464 True -ABT1_p26597266 ABT1 986.28/745.9 328.06/302.11 866.09 315.09 -1.4559 14615 21533 3.755 8.6686e-05 0.99991 0.00017337 0.003464 False -ACTR3_p114691894 ACTR3 2171/2250.1 1406.3/1163.8 2210.6 1285 -0.78214 16259 61099 3.7443 9.0443e-05 0.99991 0.00018089 0.0035432 False -ADCK5_m145608343 ADCK5 936.31/790.3 1459.9/1358.4 863.3 1409.1 0.70621 7906.4 21456 3.7263 0.9999 9.7163e-05 0.00019433 0.0037333 True -ADRBK1_p67034199 ADRBK1 923.08/758.33 1479.4/1276 840.7 1377.7 0.71192 17136 20832 3.7206 0.9999 9.9387e-05 0.00019877 0.0037467 True -ACAT2_p160183972 ACAT2 138.17/277.05 441.62/673.46 207.61 557.54 1.4209 18259 9017 3.6851 0.99988 0.00011596 0.00023192 0.0042905 True -ADRA1A_m26722399 ADRA1A 161.69/230.87 376.64/490.36 196.28 433.5 1.1391 4429.7 4230 3.6474 0.99987 0.00013263 0.00026527 0.0047957 True -AGAP2_p58129171 AGAP2 508.58/907.51 1324.9/1248.5 708.04 1286.7 0.86084 41245 25220 3.6436 0.99987 0.00013441 0.00026883 0.0047957 True -ACVR1C_m158443841 ACVR1C 474.77/515.02 895.23/869.72 494.9 882.47 0.83315 567.92 11553 3.606 0.99984 0.0001555 0.00031101 0.0054509 True -AATF_m35306482 AATF 836.36/1289.3 468.75/177.95 1062.8 323.35 -1.7137 72440 42175 3.6011 0.00015844 0.99984 0.00031689 0.0054581 False -ADRBK1_p67034193 ADRBK1 908.38/832.92 1639.7/1268.5 870.65 1454.1 0.7393 35863 26394 3.5913 0.99984 0.00016449 0.00032897 0.0055702 True -ACIN1_m23538715 ACIN1 846.65/985.65 335/415.98 916.15 375.49 -1.2845 6469.8 22922 3.5711 0.00017778 0.99982 0.00035555 0.0059199 False -ADRA1A_m26722428 ADRA1A 0/0 68.136/184.24 4.4399 126.19 4.5473 3370.2 1164.4 3.568 0.99967 0.00032598 0.00065196 0.0095886 True -ADAP1_m994071 ADAP1 1148/719.26 262.45/345.03 933.61 303.74 -1.6168 47653 31490 3.5496 0.00019291 0.99981 0.00038582 0.0063186 False -ADRB3_m37823829 ADRB3 314.55/280.6 690.82/507.52 297.58 599.17 1.0073 8687.9 7269.7 3.5373 0.9998 0.00020218 0.00040435 0.0065153 True -ADNP2_p77891042 ADNP2 238.12/232.65 417.65/547.01 235.38 482.33 1.0319 4190.7 5054.9 3.4733 0.99974 0.00025717 0.00051434 0.008156 True -ACVR1_m158637033 ACVR1 958.36/756.55 1619.5/1261.1 857.45 1440.3 0.74755 42295 28294 3.465 0.99973 0.00026516 0.00053033 0.0082203 True -ADD1_p2877627 ADD1 236.65/310.79 502.82/804.49 273.72 653.65 1.2528 24125 12027 3.4644 0.99973 0.00026743 0.00053485 0.0082203 True -ABCB8_p150730712 ABCB8 249.88/381.83 630.26/574.47 315.85 602.36 0.92921 5130.8 7010.3 3.4219 0.99969 0.0003109 0.00062181 0.0094119 True -AHNAK2_m105423836 AHNAK2 293.97/816.94 1369/1079.1 555.46 1224.1 1.1385 89382 38551 3.4054 0.99967 0.00033114 0.00066227 0.0095886 True -ACVRL1_m52306276 ACVRL1 921.61/880.87 1418.9/1405.3 901.24 1412.1 0.64725 461.12 22507 3.405 0.99967 0.00033082 0.00066164 0.0095886 True -A1CF_m52596017 A1CF 432.14/731.69 980.4/1082 581.92 1031.2 0.82436 25013 17560 3.3905 0.99965 0.00034889 0.00069778 0.0099583 True -ACVR1C_p158443781 ACVR1C 492.41/459.97 729.31/981.29 476.19 855.3 0.84356 16137 12757 3.3565 0.99961 0.0003947 0.00078941 0.011107 True -AFF4_p132272844 AFF4 599.71/941.25 1211.3/1385.3 770.48 1298.3 0.75201 36727 24846 3.3485 0.99959 0.00040631 0.00081262 0.011275 True -ACHE_m100491767 ACHE 1556.6/1758.2 970.94/942.95 1657.4 956.95 -0.79177 10356 44340 3.3264 0.00043988 0.99956 0.00087977 0.01204 False -AATK_m79102281 AATK 683.49/816.94 1095.2/1516.9 750.21 1306 0.79901 48894 28533 3.2905 0.9995 0.00050001 0.001 0.0135 True -AFAP1L2_p116100418 AFAP1L2 388.05/362.29 687.67/664.87 375.17 676.27 0.84835 295.7 8489.3 3.268 0.99946 0.0005416 0.0010832 0.014428 True -ADCK5_m145608301 ADCK5 1051/703.28 1509.7/1321.2 877.12 1415.4 0.68979 39109 27595 3.2406 0.9994 0.00059631 0.0011926 0.01567 True -ADNP_p49510899 ADNP 1491.9/1332 2087.6/1983.2 1411.9 2035.4 0.52732 9123.3 37095 3.237 0.9994 0.00060389 0.0012078 0.01567 True -AFMID_m76187074 AFMID 898.09/880.87 506.6/310.69 889.48 408.65 -1.1202 9669.3 22181 3.2285 0.00062217 0.99938 0.0012443 0.015746 False -ACSS2_p33500928 ACSS2 399.8/806.28 1147.6/1059.1 603.04 1103.3 0.87047 43263 24017 3.2283 0.99938 0.00062259 0.0012452 0.015746 True -ACSS2_m33500924 ACSS2 595.3/591.39 1061.8/887.45 593.34 974.62 0.71502 7601.9 14136 3.2068 0.99933 0.0006711 0.0013422 0.016501 True -ACTR5_p37377211 ACTR5 1021.6/738.79 476.32/281.51 880.18 378.92 -1.2137 29477 24441 3.2063 0.00067225 0.99933 0.0013445 0.016501 False -ACVR1_m158637046 ACVR1 712.89/678.41 1296.5/986.44 695.65 1141.5 0.71364 24328 19358 3.2042 0.99932 0.00067723 0.0013545 0.016501 True -ACTL6B_p100253066 ACTL6B 983.34/1022.9 560.86/438.86 1003.1 499.86 -1.0035 4113 25357 3.1605 0.00078736 0.99921 0.0015747 0.018954 False -ACIN1_p23538735 ACIN1 246.94/177.59 28.39/0 212.27 14.195 -3.811 1403.6 4505.9 3.1549 0.00080283 0.9992 0.0016057 0.019092 False -ACTR1A_p104250290 ACTR1A 605.59/738.79 2045.3/1120.9 672.19 1583.1 1.2346 2.1808e+05 83522 3.152 0.99918 0.000819 0.001638 0.019092 True -ADH5_p100003219 ADH5 316.02/586.06 44.793/127.6 451.04 86.195 -2.3741 19945 13595 3.1481 0.00082178 0.99918 0.0016436 0.019092 False -ACTR8_m53916121 ACTR8 285.15/408.47 99.05/44.63 346.81 71.84 -2.2555 4541.9 7778.6 3.1316 0.00086926 0.99913 0.0017385 0.019963 False -ACTL6A_p179287878 ACTL6A 518.86/502.59 99.05/238.6 510.73 168.82 -1.5913 4934.7 11964 3.1263 0.00088515 0.99911 0.0017703 0.020097 False -ADRB3_p37823908 ADRB3 471.83/399.59 733.09/761.57 435.71 747.33 0.77701 1507.4 10026 3.1122 0.99907 0.00092859 0.0018572 0.020846 True -AGFG1_m228337276 AGFG1 2382.7/2260.8 1567.8/1499.1 2321.7 1533.4 -0.59811 4891.7 64529 3.1032 0.00095731 0.99904 0.0019146 0.021204 False -ACTL7A_m111624736 ACTL7A 873.1/829.37 364.65/436.57 851.24 400.61 -1.0854 1771.3 21122 3.1006 0.00096573 0.99903 0.0019315 0.021204 False -ADAM10_p58974401 ADAM10 968.64/864.89 590.51/256.34 916.77 423.43 -1.1126 30610 25496 3.0897 0.001002 0.999 0.0020039 0.02176 False -AFF3_m100625363 AFF3 1159.7/761.88 505.34/304.97 960.8 405.16 -1.2437 49608 32648 3.0752 0.0010519 0.99895 0.0021038 0.022599 False -ADCK3_p227149165 ADCK3 558.55/838.25 1063.7/1170.7 698.4 1117.2 0.67697 22420 18772 3.0566 0.99888 0.0011193 0.0022387 0.023792 True -ABT1_p26597300 ABT1 1562.5/763.66 422.07/220.29 1163.1 321.18 -1.8532 1.697e+05 76498 3.0473 0.0011547 0.99885 0.0023093 0.02425 False -AGBL5_m27275887 AGBL5 640.86/602.05 307.24/192.83 621.45 250.03 -1.3101 3649.6 14883 3.0445 0.0011652 0.99883 0.0023304 0.02425 False -ADARB1_m46595721 ADARB1 229.3/735.24 1186.1/914.35 482.27 1050.2 1.1211 82453 34968 3.0372 0.9988 0.0012001 0.0024001 0.024531 True -ABHD14B_m52004122 ABHD14B 1793.2/1836.3 983.56/1301.7 1814.8 1142.6 -0.66697 25770 49052 3.0349 0.0012032 0.9988 0.0024065 0.024531 False -ADK_p76154015 ADK 454.19/490.16 896.49/685.47 472.18 790.98 0.74309 11456 11128 3.0222 0.99875 0.0012548 0.0025096 0.025324 True -AGPAT5_m6566233 AGPAT5 3338.1/3873.3 2840.3/2366 3605.7 2603.1 -0.46989 1.2787e+05 1.1285e+05 2.9845 0.0014203 0.99858 0.0028405 0.028377 False -ACRC_p70800703 ACRC 1393.4/1472.3 1039.7/532.7 1432.8 786.2 -0.86508 65817 47077 2.9803 0.0014398 0.99856 0.0028796 0.028482 False -ADK_m75960590 ADK 208.72/65.71 292.1/350.17 137.22 321.14 1.2208 5956.2 3834.8 2.9701 0.99849 0.0015089 0.0030177 0.029556 True -ACTR1A_p104248866 ACTR1A 975.99/665.98 467.49/172.23 820.99 319.86 -1.3572 45822 28800 2.9531 0.0015732 0.99843 0.0031463 0.030516 False -ACSL6_m131329908 ACSL6 1011.3/866.66 557.71/419.98 938.97 488.84 -0.94029 9970 23558 2.9326 0.0016805 0.99832 0.0033609 0.032284 False -ACTN1_m69445700 ACTN1 699.66/571.86 1036.6/953.25 635.76 994.9 0.64526 5818.1 15265 2.9069 0.99817 0.0018254 0.0036508 0.034735 True -ADH5_m100006267 ADH5 1339.1/1610.8 2090.1/2003.8 1474.9 2047 0.47257 20324 38942 2.8988 0.99813 0.0018729 0.0037458 0.035196 True -ACO2_p41903797 ACO2 567.37/665.98 377.27/100.13 616.68 238.7 -1.3656 21633 17048 2.895 0.0018957 0.9981 0.0037915 0.035196 False -ACBD6_m180471338 ACBD6 1123/815.16 436.58/573.33 969.07 504.95 -0.93909 28364 25721 2.8939 0.0019025 0.9981 0.003805 0.035196 False -ACTL6B_m100253086 ACTL6B 637.92/925.27 1187.3/1186.7 781.6 1187 0.60222 20642 19686 2.8895 0.99807 0.0019291 0.0038582 0.035361 True -ADAR_m154574103 ADAR 132.29/133.2 278.22/658.58 132.74 468.4 1.8114 36168 13839 2.8533 0.99751 0.0024852 0.0049703 0.043556 True -ADCK3_p227149155 ADCK3 2275.4/2228.8 2932.4/2994.2 2252.1 2963.3 0.39579 1498 62378 2.8476 0.9978 0.0022023 0.0044046 0.040002 True -ADARB1_p46595645 ADARB1 1084.8/1005.2 1514.8/1497.4 1045 1506.1 0.52691 1658.6 26537 2.8306 0.99768 0.002323 0.004646 0.04146 True -ACAD9_m128598636 ACAD9 570.31/527.46 308.51/144.76 548.88 226.63 -1.2724 7162.1 12963 2.8305 0.0023241 0.99768 0.0046481 0.04146 False -ACVRL1_p52306296 ACVRL1 1331.7/1124.2 725.52/727.81 1227.9 726.67 -0.75606 10768 31756 2.8129 0.0024547 0.99755 0.0049093 0.043402 False -ACO2_m41895810 ACO2 2229.8/1791.9 1452.9/1241.6 2010.9 1347.3 -0.5774 59094 56356 2.7953 0.0025929 0.99741 0.0051858 0.045049 False -AHRR_m344006 AHRR 345.42/163.39 474.43/481.2 254.4 477.82 0.9067 8295.4 6439.2 2.7842 0.99731 0.0026854 0.0053709 0.045895 True -ADD3_m111860427 ADD3 283.69/518.58 682.62/695.77 401.13 689.2 0.77934 13837 10709 2.7837 0.99731 0.0026875 0.0053751 0.045895 True -AATF_p35306410 AATF 2096/1960.6 1573.4/629.97 2028.3 1101.7 -0.87996 2.2712e+05 1.1272e+05 2.76 0.0028899 0.99711 0.0057797 0.048932 False -ACVR1B_m52369170 ACVR1B 411.56/831.14 970.31/1637 621.35 1303.7 1.0679 1.5513e+05 61632 2.7484 0.99699 0.0030131 0.0060263 0.050502 True -ABI1_p27149701 ABI1 698.19/779.64 1791.1/1034.5 738.91 1412.8 0.93415 1.4477e+05 60285 2.7446 0.99697 0.0030332 0.0060664 0.050502 True -AHCY_p32883304 AHCY 1625.7/1410.1 2146.3/1986.6 1517.9 2066.5 0.44484 17992 40206 2.7358 0.99689 0.0031119 0.0062238 0.051385 True -AHNAK2_p105423983 AHNAK2 363.06/388.93 977.25/556.16 376 766.7 1.026 44496 20505 2.7285 0.9968 0.0031953 0.0063905 0.052329 True -ACO2_m41895784 ACO2 824.6/866.66 1135/1341.2 845.63 1238.1 0.54947 11074 20967 2.7103 0.99664 0.0033614 0.0067228 0.054602 True -AHNAK_p62303470 AHNAK 379.23/731.69 881.35/997.31 555.46 939.33 0.7569 34419 20230 2.6989 0.99652 0.0034785 0.0069571 0.056049 True -ADCK1_m78285413 ADCK1 1193.5/1355 1622/1905.9 1274.3 1764 0.46882 26674 33093 2.6918 0.99645 0.003553 0.007106 0.056334 True -ACVR2A_m148602752 ACVR2A 1403.7/996.31 745.71/644.28 1200 694.99 -0.78711 44070 35326 2.687 0.003605 0.9964 0.00721 0.056334 False -ADCK5_p145608311 ADCK5 601.18/742.35 1225.2/901.19 671.76 1063.2 0.66158 31227 21229 2.6865 0.99639 0.0036103 0.0072206 0.056334 True -ACHE_m100491773 ACHE 564.43/388.93 162.14/226.01 476.68 194.08 -1.292 8719.8 11080 2.685 0.0036263 0.99637 0.0072527 0.056334 False -AATK_p79104864 AATK 877.51/1136.6 1415.7/1455.1 1007.1 1435.4 0.51086 17169 25467 2.684 0.99636 0.0036372 0.0072744 0.056334 True -ADRB3_m37823894 ADRB3 1215.6/1864.7 2161.4/2267.6 1540.2 2214.5 0.52361 1.0817e+05 63299 2.6802 0.99632 0.0036785 0.0073569 0.056535 True -ADI1_m3523246 ADI1 801.08/665.98 1251.7/971.56 733.53 1111.6 0.59907 24180 19992 2.674 0.99625 0.0037472 0.0074944 0.056772 True -ADK_m76154033 ADK 557.08/685.52 875.04/1019.6 621.3 947.34 0.60779 9349.9 14879 2.6729 0.99624 0.0037603 0.0075206 0.056772 True -ACTL6A_p179287966 ACTL6A 1478.7/728.14 548.24/173.94 1103.4 361.09 -1.6088 1.7586e+05 77415 2.6712 0.0037791 0.99622 0.0075583 0.056772 False -AEBP1_m44144319 AEBP1 992.16/896.85 488.94/579.05 944.51 533.99 -0.82157 4300.8 23713 2.6658 0.0038398 0.99616 0.0076796 0.057253 False -AAAS_m53714382 AAAS 1034.8/1191.7 504.08/815.93 1113.2 660.01 -0.75331 30465 29136 2.6552 0.0039636 0.99604 0.0079272 0.058661 False -AATF_p35306466 AATF 277.81/435.11 182.33/0 356.46 91.164 -1.9555 14497 10179 2.6462 0.0040705 0.99593 0.008141 0.0598 False -ACTR3_m114688941 ACTR3 1148/1046 811.32/186.53 1097 498.93 -1.1351 1.0019e+05 52070 2.621 0.0043835 0.99562 0.0087669 0.063928 False -ACTL7A_p111624631 ACTL7A 659.97/717.48 377.9/325 688.73 351.45 -0.9686 1526.6 16687 2.611 0.0045142 0.99549 0.0090284 0.065358 False -AEN_m89169489 AEN 94.072/0 175.39/122.45 47.036 148.92 1.642 2913 1533.7 2.6015 0.99476 0.0052434 0.010487 0.071756 True -ACTL6A_m179287950 ACTL6A 380.7/387.16 191.79/90.977 383.93 141.38 -1.4348 2551.3 8710 2.6003 0.0046575 0.99534 0.0093151 0.066777 False -AFF1_p87967338 AFF1 620.29/641.12 825.83/1159.8 630.7 992.82 0.65375 27994 19418 2.5987 0.99532 0.0046791 0.0093581 0.066777 True -ACTL7A_p111624691 ACTL7A 335.13/497.27 601.24/779.88 416.2 690.56 0.72912 14550 11202 2.5922 0.99523 0.004768 0.0095361 0.067564 True -ACTR8_m53916057 ACTR8 992.16/1005.2 743.82/200.26 998.67 472.04 -1.0795 73906 41456 2.5865 0.0048472 0.99515 0.0096944 0.068202 False -AAK1_m69870056 AAK1 933.37/1255.6 1409.4/2011.2 1094.5 1710.3 0.64354 1.165e+05 57460 2.5691 0.9949 0.0050983 0.010197 0.071234 True -ADAD1_m123301366 ADAD1 526.21/557.65 897.76/765.01 541.93 831.38 0.61648 4652.6 12780 2.5604 0.99477 0.005228 0.010456 0.071756 True -ACAT2_p160183107 ACAT2 2034.3/2248.3 1099.6/1749.7 2141.3 1424.7 -0.58752 1.1711e+05 78351 2.5602 0.0052302 0.99477 0.01046 0.071756 False -AGL_p100327219 AGL 196.96/287.7 41.639/78.389 242.33 60.014 -1.9957 2396.1 5221.1 2.5479 0.0054181 0.99458 0.010836 0.073642 False -AHNAK_m62303488 AHNAK 399.8/504.37 781.04/643.13 452.09 712.09 0.65429 7488.2 10446 2.5438 0.99452 0.0054821 0.010964 0.074008 True -ADCK3_m227149163 ADCK3 1115.6/1063.8 1343.8/1829.3 1089.7 1586.5 0.54151 59592 38400 2.5353 0.99438 0.0056173 0.011235 0.075325 True -ACAD9_p128598545 ACAD9 829.01/1099.3 676.31/403.96 964.16 540.14 -0.83477 36810 28445 2.5141 0.0059667 0.99403 0.011933 0.079477 False -ACAD9_p128598592 ACAD9 921.61/774.31 499.03/469.76 847.96 484.4 -0.80653 5638.3 21032 2.5069 0.0060892 0.99391 0.012178 0.080572 False -ACTN4_p39138482 ACTN4 1362.6/1005.2 1596.2/1653.6 1183.9 1624.9 0.45648 32756 31246 2.4948 0.9937 0.0063007 0.012601 0.082247 True -AFTPH_m64778905 AFTPH 463.01/353.41 618.9/679.18 408.21 649.04 0.66768 3911.1 9324.9 2.4939 0.99368 0.0063167 0.012633 0.082247 True -ABT1_p26597221 ABT1 1597.7/1465.2 1065.6/992.73 1531.5 1029.2 -0.57298 5721.6 40607 2.4927 0.0063393 0.99366 0.012679 0.082247 False -AES_m3056311 AES 756.98/616.25 892.08/1161.5 686.62 1026.8 0.57988 23102 18787 2.4819 0.99347 0.0065342 0.013068 0.083824 True -AEBP2_p19615465 AEBP2 257.23/429.78 158.98/81.822 343.5 120.4 -1.5047 8932 8108.1 2.4813 0.0065448 0.99346 0.01309 0.083824 False -ADAP1_m994066 ADAP1 167.57/67.486 204.41/355.9 117.53 280.15 1.2462 8241.2 4303.9 2.4789 0.99316 0.0068396 0.013679 0.085409 True -ACTR3_m114684932 ACTR3 1024.5/1362.2 883.88/396.52 1193.3 640.2 -0.89735 87881 49801 2.4786 0.0065951 0.9934 0.01319 0.08393 False -A1CF_p52595881 A1CF 714.36/671.31 1119.8/906.91 692.83 1013.4 0.54792 11797 16797 2.4732 0.9933 0.0066957 0.013391 0.08445 True -ACLY_m40070097 ACLY 495.35/586.06 279.48/243.75 540.7 261.62 -1.0445 2376.6 12748 2.4719 0.0067205 0.99328 0.013441 0.08445 False -ADCY1_m45614243 ADCY1 1011.3/1211.2 485.15/843.4 1111.2 664.27 -0.74144 42077 32969 2.4616 0.0069165 0.99308 0.013833 0.085833 False -ACTL6A_p179287892 ACTL6A 415.97/669.53 750.76/1074.6 542.75 912.66 0.7487 42284 22629 2.459 0.99303 0.0069678 0.013936 0.085936 True -ADAMTS5_p28338585 ADAMTS5 973.05/939.48 538.15/613.38 956.27 575.76 -0.73094 1696.7 24042 2.454 0.0070641 0.99294 0.014128 0.086472 False -AGL_m100327128 AGL 122/248.63 318.6/366.2 185.32 342.4 0.88213 4575.4 4108.1 2.4508 0.99286 0.0071411 0.014282 0.086472 True -AHNAK2_m105444536 AHNAK2 382.17/454.64 707.86/608.8 418.4 658.33 0.65266 3766.3 9584.3 2.4507 0.99287 0.0071284 0.014257 0.086472 True -ADCK1_m78285328 ADCK1 662.91/1008.7 1101.5/1694.8 835.82 1398.2 0.74157 1.1789e+05 53095 2.4405 0.99266 0.0073351 0.01467 0.088286 True -ADAP1_m994042 ADAP1 292.5/369.4 454.24/709.5 330.95 581.87 0.81221 17768 10845 2.4094 0.99201 0.0079943 0.015989 0.095645 True -ADNP2_p77875475 ADNP2 248.41/106.56 286.42/461.18 177.48 373.8 1.0703 12665 6683.6 2.4013 0.99171 0.0082915 0.016583 0.097623 True -AEBP1_m44144326 AEBP1 1149.4/1149 1616.3/1506.6 1149.2 1561.4 0.44187 3013.3 29499 2.4 0.9918 0.0081976 0.016395 0.097493 True -ACVR2B_m38518828 ACVR2B 404.21/1095.8 1399.9/1221 749.99 1310.5 0.80435 1.2756e+05 54751 2.3954 0.99169 0.0083063 0.016613 0.097623 True -ABI1_m27149751 ABI1 1233.2/868.44 720.48/531.56 1050.8 626.02 -0.74633 42189 31864 2.3798 0.0086604 0.99134 0.017321 0.10099 False -ABCB8_m150725643 ABCB8 598.24/1035.4 1096.5/1688.5 816.81 1392.5 0.76888 1.3539e+05 58581 2.3785 0.99131 0.0086939 0.017388 0.10099 True -AGFG1_p228337221 AGFG1 449.78/428 713.54/641.99 438.89 677.76 0.62576 1398.4 10108 2.3759 0.99125 0.0087524 0.017505 0.10108 True -ADRB3_p37823813 ADRB3 1267/703.28 528.05/453.17 985.15 490.61 -1.0043 80857 43520 2.3707 0.0088784 0.99112 0.017757 0.10195 False -ACTRT3_p169487206 ACTRT3 35.277/406.69 451.09/596.79 220.98 523.94 1.2417 39794 16406 2.3652 0.99059 0.0094075 0.018815 0.1068 True -ACD_m67694376 ACD 1855/1420.8 1083.9/1183.8 1637.9 1133.9 -0.53019 49635 45718 2.3572 0.0092066 0.99079 0.018413 0.10511 False -AFF3_p100623773 AFF3 1902/1825.7 1573.4/850.83 1863.8 1212.1 -0.62031 1.32e+05 77686 2.3382 0.0096884 0.99031 0.019377 0.10936 False -AHNAK_m62303548 AHNAK 868.69/980.32 721.11/327.86 924.51 524.48 -0.8166 41776 29362 2.3345 0.0097848 0.99022 0.01957 0.10983 False -ACTRT3_m169487242 ACTRT3 2171/1610.8 1054.8/1460.2 1890.9 1257.5 -0.58809 1.1954e+05 74078 2.3271 0.0099808 0.99002 0.019962 0.11141 False -ACTR1A_m104250350 ACTR1A 417.44/223.77 119.24/110.43 320.61 114.83 -1.4732 9396.8 7884.1 2.323 0.010089 0.98991 0.020177 0.11198 False -ADCK3_p227149104 ADCK3 1628.6/1669.4 2129.3/2142.2 1649 2135.7 0.37295 457.83 44091 2.3181 0.98978 0.010222 0.020445 0.11284 True -AHNAK2_m105423829 AHNAK2 737.88/879.09 561.49/402.82 808.48 482.15 -0.74452 11280 19945 2.3107 0.010425 0.98957 0.020851 0.11445 False -ACRC_p70814200 ACRC 1940.2/1289.3 942.55/1103.7 1614.8 1023.1 -0.65782 1.1241e+05 66186 2.2997 0.010732 0.98927 0.021464 0.11689 False -AGPAT3_p45379592 AGPAT3 1078.9/451.09 300.3/251.19 764.99 275.75 -1.4688 99135 45548 2.2982 0.010774 0.98923 0.021549 0.11689 False -ADNP_p49511011 ADNP 427.73/573.63 655.49/899.47 500.68 777.48 0.63389 20202 14536 2.2958 0.98916 0.010843 0.021685 0.11689 True -ADCK2_m140373210 ADCK2 862.81/628.69 1499.6/940.67 745.75 1220.1 0.70954 91813 42758 2.2942 0.98911 0.010891 0.021782 0.11689 True -ADH5_m100003154 ADH5 560.02/753 262.45/456.6 656.51 359.53 -0.86691 18734 16792 2.2919 0.010956 0.98904 0.021913 0.11689 False -AGPAT5_m6566202 AGPAT5 286.62/570.08 193.68/129.89 428.35 161.78 -1.3992 21104 13593 2.2904 0.010999 0.989 0.021998 0.11689 False -ADCK4_p41220480 ADCK4 2523.8/2445.5 2153.2/1516.9 2484.6 1835 -0.43701 1.0278e+05 80651 2.2873 0.011088 0.98891 0.022176 0.11722 False -AGAP3_p150784007 AGAP3 707.01/635.79 837.19/1465.4 671.4 1151.3 0.77709 99916 44119 2.2846 0.98883 0.011175 0.02235 0.11751 True -ACBD6_p180471279 ACBD6 792.26/1243.2 681.99/462.32 1017.7 572.16 -0.82974 62892 38142 2.2814 0.011263 0.98874 0.022525 0.11782 False -ABL2_p179100524 ABL2 692.31/472.4 755.81/1107.2 582.36 931.49 0.67671 42954 23548 2.2752 0.98855 0.011449 0.022898 0.11914 True -ACIN1_p23538701 ACIN1 405.68/268.17 170.34/109.86 336.93 140.1 -1.26 5642.2 7532.4 2.2695 0.011618 0.98838 0.023235 0.12027 False -ADAR_p154574114 ADAR 99.951/195.35 38.484/20.026 147.65 29.255 -2.2967 2360.6 3009.6 2.259 0.011943 0.98806 0.023885 0.123 False -ADAM12_p128076641 ADAM12 1128.9/1333.7 1475.7/1837.3 1231.3 1656.5 0.42762 43186 35631 2.2524 0.98785 0.012149 0.024297 0.12417 True -ABLIM2_p8108299 ABLIM2 939.25/1484.7 1625.2/1782.3 1212 1703.8 0.49102 80553 47716 2.2514 0.98782 0.012181 0.024362 0.12417 True -ACVR2A_p148602720 ACVR2A 1412.5/715.71 1485.1/1809.8 1064.1 1647.5 0.6301 1.4775e+05 67303 2.2485 0.98773 0.012271 0.024542 0.1243 True -ACTN4_m39138430 ACTN4 163.16/264.62 376.01/354.75 213.89 365.38 0.76978 2686.5 4544.2 2.2474 0.98768 0.012318 0.024636 0.1243 True -AATF_m35306509 AATF 367.47/571.86 300.3/116.72 469.66 208.51 -1.1676 18869 13556 2.2438 0.012422 0.98758 0.024844 0.12472 False -ACAD11_p132378460 ACAD11 1621.3/1113.5 816.37/951.54 1367.4 883.95 -0.62881 69020 46870 2.233 0.012773 0.98723 0.025547 0.12761 False -AHNAK_m62303560 AHNAK 704.07/959.01 1027.7/1504.8 831.54 1266.3 0.60614 73159 38106 2.2271 0.98703 0.012972 0.025943 0.12879 True -ACTN1_m69392359 ACTN1 545.32/353.41 897.76/593.35 449.37 745.55 0.72914 32373 17709 2.2257 0.98698 0.013021 0.026042 0.12879 True -ADRBK2_m26057580 ADRBK2 610/815.16 1209.4/892.6 712.58 1051 0.56 35616 23426 2.2112 0.98649 0.013512 0.027024 0.13299 True -ABCB8_p150725612 ABCB8 368.94/190.03 700.29/391.94 279.48 546.12 0.96394 31771 14669 2.2015 0.986 0.013999 0.027998 0.13711 True -ABCF1_p30545587 ABCF1 48.506/248.63 665.59/248.9 148.57 457.24 1.6153 53420 19827 2.1922 0.9834 0.016603 0.033206 0.15509 True -ACVRL1_p52306281 ACVRL1 1195/896.85 1687.6/1273.1 1045.9 1480.4 0.50076 65182 39436 2.1877 0.98565 0.014347 0.028694 0.13983 True -AHCY_m32883210 AHCY 301.32/657.1 228.38/137.32 479.21 182.85 -1.3851 33717 18670 2.1749 0.01482 0.98518 0.02964 0.14325 False -ADAMTS5_p28338667 ADAMTS5 1115.6/923.49 1207.5/1932.8 1019.6 1570.2 0.62248 1.4075e+05 64128 2.1743 0.98516 0.014841 0.029683 0.14325 True -ADNP_p49520498 ADNP 690.84/767.21 1040.3/993.31 729.02 1016.8 0.47947 2011.1 17776 2.1586 0.98456 0.015442 0.030884 0.14833 True -ACSL6_p131329830 ACSL6 770.21/735.24 519.85/403.96 752.73 461.91 -0.70332 3663.5 18421 2.1428 0.016066 0.98393 0.032133 0.15359 False -AFTPH_m64778617 AFTPH 415.97/293.03 242.89/37.192 354.5 140.04 -1.3337 14357 10099 2.1391 0.016214 0.98379 0.032429 0.15398 False -ADCK5_p145603106 ADCK5 1483.1/1340.8 2315.4/1616.4 1412 1965.9 0.47718 1.2719e+05 67129 2.1379 0.98374 0.016261 0.032523 0.15398 True -ACLY_m40069993 ACLY 1268.5/1411.9 1136.2/603.65 1340.2 869.94 -0.62286 76050 48686 2.1312 0.016536 0.98346 0.033072 0.15509 False -ADNP_m49520507 ADNP 959.83/1397.7 1429.6/2811.1 1178.7 2120.4 0.84651 5.2508e+05 1.9526e+05 2.1309 0.98339 0.016611 0.033222 0.15509 True -AAK1_m69870103 AAK1 712.89/802.73 576.63/359.9 757.81 468.27 -0.69332 13761 18559 2.1254 0.016779 0.98322 0.033557 0.15592 False -ADCK1_p78285416 ADCK1 826.07/797.4 977.88/2066.7 811.73 1522.3 0.90634 2.966e+05 1.1222e+05 2.1211 0.98291 0.017088 0.034175 0.15806 True -ACLY_p40070062 ACLY 512.98/269.94 205.67/126.45 391.46 166.06 -1.2322 16336 11379 2.1158 0.017179 0.98282 0.034358 0.15817 False -AGL_m100327263 AGL 758.45/463.52 410.08/210.56 610.99 310.32 -0.9751 31698 20302 2.1104 0.017414 0.98259 0.034828 0.1596 False -ADAM12_p128019039 ADAM12 1824.1/815.16 661.8/605.37 1319.6 633.59 -1.0573 2.5529e+05 1.0803e+05 2.0879 0.018404 0.9816 0.036807 0.16759 False -ACBD6_p180471344 ACBD6 1040.7/1292.9 1392.4/1703.4 1166.8 1547.9 0.40746 40086 33362 2.0865 0.98153 0.018469 0.036937 0.16759 True -ADRM1_p60878615 ADRM1 477.71/841.8 176.65/448.02 659.75 312.33 -1.0764 51551 27789 2.0849 0.018538 0.98146 0.037075 0.16759 False -ACTL6B_m100253443 ACTL6B 1114.2/1284 1689.5/1439.6 1199.1 1564.6 0.38355 22826 30927 2.0782 0.98116 0.018844 0.037687 0.16959 True -ADRB1_m115803922 ADRB1 230.77/223.77 388.63/354.75 227.27 371.69 0.70724 299.14 4861.5 2.0713 0.98082 0.019175 0.038351 0.1717 True -ABCC1_m16101705 ABCC1 1283.2/1053.1 832.77/786.18 1168.2 809.48 -0.52864 13775 30041 2.0695 0.019249 0.98075 0.038498 0.1717 False -ACTR8_m53916047 ACTR8 2484.1/2349.6 2172.8/910.91 2416.8 1541.8 -0.64812 4.026e+05 1.7919e+05 2.067 0.019366 0.98063 0.038732 0.17197 False -AATF_m35306475 AATF 724.65/1086.9 695.24/307.26 905.76 501.25 -0.85232 70435 38567 2.0598 0.019707 0.98029 0.039415 0.17423 False -ABLIM2_p8108286 ABLIM2 936.31/996.31 1115.4/1572.9 966.31 1344.2 0.47574 53230 33959 2.0505 0.97984 0.020158 0.040316 0.17742 True -AGPAT3_p45379648 AGPAT3 1030.4/657.1 589.25/411.4 843.74 500.32 -0.75276 42742 28191 2.0453 0.020411 0.97959 0.040821 0.1784 False -ADRBK1_p67034166 ADRBK1 1270/1213 1591.1/1625 1241.5 1608 0.373 1099.3 32146 2.0446 0.97955 0.020448 0.040896 0.1784 True -ADD3_p111860505 ADD3 977.46/845.35 620.8/585.34 911.41 603.07 -0.59497 4677.7 22790 2.0425 0.020553 0.97945 0.041105 0.17854 False -AFTPH_m64778773 AFTPH 60.265/161.61 231.54/191.68 110.94 211.61 0.9255 2964.9 2448.4 2.0345 0.97878 0.021215 0.042431 0.1835 True -ADH5_p100002510 ADH5 443.9/241.53 451.72/726.67 342.71 589.19 0.77998 29138 14830 2.024 0.97846 0.02154 0.04308 0.1855 True -ACTR3_p114684941 ACTR3 952.48/1094 1239.1/1457.9 1023.2 1348.5 0.39788 16980 25923 2.0202 0.97832 0.021681 0.043363 0.18592 True -ADCY1_m45614323 ADCY1 709.95/912.84 1003.7/1190.1 811.39 1096.9 0.43455 18976 20025 2.0179 0.9782 0.021802 0.043604 0.18616 True -ABL1_m133729449 ABL1 599.71/374.72 1384.2/601.36 487.22 992.77 1.0254 1.6585e+05 62853 2.0165 0.97754 0.022456 0.044913 0.19012 True -AAK1_m69870137 AAK1 1330.2/522.13 323.02/456.6 926.18 389.81 -1.2464 1.6772e+05 71374 2.0126 0.022081 0.97792 0.044162 0.18773 False -A1CF_m52603847 A1CF 0/88.797 91.479/152.2 44.399 121.84 1.4361 2893 1492.1 2.0048 0.97429 0.025711 0.051423 0.20798 True -ADRB1_m115804047 ADRB1 637.92/545.22 385.47/322.14 591.57 353.81 -0.73996 3151.5 14089 2.0031 0.022583 0.97742 0.045166 0.19038 False -AGPAT3_m45379563 AGPAT3 263.11/250.41 51.102/164.79 256.76 107.95 -1.2424 3271.4 5567.8 1.9995 0.022775 0.97723 0.04555 0.19119 False -ACO2_m41865159 ACO2 1192.1/879.09 852.33/377.07 1035.6 614.7 -0.75153 80957 44500 1.9952 0.023012 0.97699 0.046024 0.19237 False -ADK_m76153995 ADK 1011.3/1166.8 652.97/860.56 1089 756.77 -0.52455 16820 27785 1.9934 0.023111 0.97689 0.046221 0.1924 False -ADRA1A_p26722406 ADRA1A 333.66/557.65 632.15/680.9 445.65 656.52 0.55788 13137 11233 1.9896 0.97668 0.023318 0.046635 0.19331 True -ABCC1_m16101799 ABCC1 730.53/907.51 491.46/585.34 819.02 538.4 -0.60429 10034 20235 1.9727 0.024264 0.97574 0.048528 0.19967 False -AFF4_m132272833 AFF4 1365.5/1925.1 1092.1/1252.5 1645.3 1172.3 -0.48869 84728 57563 1.9716 0.024328 0.97567 0.048656 0.19967 False -ADCY1_p45614309 ADCY1 389.52/195.35 176.02/37.764 292.44 106.89 -1.4435 14203 9024.2 1.9706 0.024384 0.97562 0.048768 0.19967 False -ADNP2_m77891006 ADNP2 0/0 15.772/168.79 4.4399 92.283 4.1 5853.9 1992.3 1.968 0.95454 0.045461 0.090922 0.29049 True -AHCTF1_m247068886 AHCTF1 1095.1/882.65 837.82/365.05 988.85 601.44 -0.7164 67157 39023 1.9612 0.024929 0.97507 0.049858 0.20315 False -ADI1_p3523164 ADI1 1120/1216.5 638.46/1000.2 1168.3 819.32 -0.51137 35036 31708 1.9597 0.025013 0.97499 0.050026 0.20315 False -AGTPBP1_m88307696 AGTPBP1 0/362.29 437.84/382.79 181.15 410.31 1.1751 33572 13709 1.9573 0.97321 0.026791 0.053581 0.21411 True -ADI1_p3517642 ADI1 261.64/399.59 406.29/1138.1 330.61 772.18 1.2213 1.3863e+05 51128 1.9529 0.97261 0.027386 0.054772 0.21627 True -A1CF_m52596056 A1CF 618.82/653.55 357.08/434.29 636.18 395.68 -0.68371 1791.6 15276 1.9458 0.025838 0.97416 0.051677 0.20817 False -ACAD9_p128598519 ACAD9 636.45/754.78 581.05/224.87 695.62 402.96 -0.78616 35217 22987 1.9303 0.026785 0.97322 0.053569 0.21411 False -AGAP3_p150783825 AGAP3 326.31/635.79 960.85/615.67 481.05 788.26 0.71131 53731 25373 1.9286 0.97308 0.026923 0.053847 0.21431 True -ADAM10_m58974490 ADAM10 612.94/699.72 426.48/401.67 656.33 414.08 -0.66324 2036.9 15816 1.9263 0.027032 0.97297 0.054065 0.21433 False -ADPRHL2_m36554620 ADPRHL2 351.3/298.36 244.79/74.384 324.83 159.58 -1.0208 7959.8 7474.8 1.9126 0.0279 0.9721 0.055799 0.21946 False -ACTL7A_m111624684 ACTL7A 533.56/348.09 606.92/658.58 440.82 632.75 0.52044 9267.8 10157 1.9043 0.97157 0.028434 0.056869 0.2226 True -ACTRT3_m169487289 ACTRT3 1117.1/532.78 474.43/389.66 824.94 432.04 -0.93153 87153 42649 1.903 0.028521 0.97148 0.057042 0.2226 False -AGL_p100327060 AGL 730.53/461.75 329.96/386.22 596.14 358.09 -0.73372 18852 15758 1.8964 0.028956 0.97104 0.057912 0.22511 False -ADAM12_p128018991 ADAM12 51.445/69.262 83.277/222.58 60.354 152.93 1.327 4930.6 2385.8 1.8953 0.96745 0.032554 0.065108 0.24032 True -ADCK5_p145608305 ADCK5 376.29/619.81 376.64/94.41 498.05 235.53 -1.0772 34739 19336 1.8904 0.029353 0.97065 0.058706 0.22652 False -ADIRF_p88728321 ADIRF 1647.7/760.11 685.78/608.23 1203.9 647 -0.89486 1.9847e+05 86867 1.8899 0.029387 0.97061 0.058775 0.22652 False -ABCF1_p30545610 ABCF1 1975.5/1797.3 1628.3/1289.7 1886.4 1459 -0.37041 36611 51211 1.8885 0.029477 0.97052 0.058955 0.22652 False -ACBD6_p180471350 ACBD6 607.06/538.11 754.54/830.24 572.58 792.39 0.46802 2620.6 13587 1.8857 0.97033 0.029667 0.059334 0.22708 True -ABL1_p133729492 ABL1 1105.3/1172.1 1019.5/401.1 1138.7 710.31 -0.68015 96725 51708 1.8841 0.029778 0.97022 0.059555 0.22708 False -AGAP2_p58129148 AGAP2 762.86/1189.9 622.06/660.3 976.37 641.18 -0.60594 45952 31721 1.882 0.029916 0.97008 0.059832 0.22727 False -ADH7_m100350762 ADH7 1856.4/1578.8 1422.7/1207.9 1717.6 1315.3 -0.38481 30802 46138 1.8732 0.030518 0.96948 0.061036 0.22984 False -AFAP1L2_p116100409 AFAP1L2 1344.9/1133.1 2324.8/1356.6 1239 1840.7 0.57073 2.4557e+05 1.0324e+05 1.8728 0.96945 0.030551 0.061102 0.22984 True -AES_m3061159 AES 565.9/658.88 1000.6/715.8 612.39 858.2 0.48618 22438 17241 1.872 0.9694 0.0306 0.0612 0.22984 True -ABI1_p27112164 ABI1 1709.5/1655.2 1220.8/1354.9 1682.3 1287.8 -0.38523 5235.8 45083 1.8578 0.031596 0.9684 0.063191 0.23643 False -ADRB1_p115803909 ADRB1 577.66/634.01 706.6/969.28 605.84 837.94 0.46725 18044 15660 1.8547 0.96818 0.031817 0.063635 0.23721 True -ABCB8_p150725605 ABCB8 149.93/259.29 880.72/244.89 204.61 562.81 1.4553 1.0406e+05 37570 1.848 0.9622 0.037804 0.075607 0.26117 True -ACSL6_m131326625 ACSL6 640.86/190.03 897.13/587.06 415.45 742.09 0.83541 74849 31289 1.8466 0.96729 0.032708 0.065415 0.24032 True -ABLIM2_m8108274 ABLIM2 546.79/1392.3 1411.3/1554.6 969.57 1483 0.61255 1.8387e+05 77568 1.8434 0.96735 0.032647 0.065294 0.24032 True -ACTRT3_m169487267 ACTRT3 634.98/676.64 847.28/927.51 655.81 887.4 0.43573 2042.6 15802 1.8423 0.96728 0.032716 0.065431 0.24032 True -ADD1_p2877730 ADD1 194.02/24.863 238.48/209.42 109.44 223.95 1.0263 7364.8 3893.2 1.8351 0.96538 0.034618 0.069237 0.2506 True -ADD3_p111860500 ADD3 515.92/1049.6 1134.3/1131.8 782.75 1133.1 0.53302 71200 36560 1.8321 0.96653 0.033472 0.066944 0.24497 True -AHCTF1_m247068867 AHCTF1 748.16/488.39 239.74/477.77 618.27 358.75 -0.78357 31036 20211 1.8256 0.033958 0.96604 0.067917 0.24762 False -ADI1_m3523251 ADI1 1561/1820.3 2134.3/2023.2 1690.7 2078.8 0.29797 19899 45333 1.8228 0.96583 0.03417 0.06834 0.24826 True -AGFG1_m228337248 AGFG1 983.34/671.31 675.68/335.87 827.33 505.78 -0.70885 53210 31379 1.8152 0.034743 0.96526 0.069486 0.2506 False -ABT1_p26597313 ABT1 30.867/179.37 238.48/184.81 105.12 211.65 1.0028 6233.2 3453 1.8128 0.96374 0.036264 0.072527 0.25877 True -AATK_m79102298 AATK 1067.1/1417.2 1601.2/1531.2 1242.2 1566.2 0.33415 31865 32166 1.8066 0.96459 0.035412 0.070824 0.25451 True -ADCK4_m41220284 ADCK4 1234.7/1300 1357.7/2232.7 1267.3 1795.2 0.50197 1.9246e+05 86082 1.799 0.96399 0.03601 0.07202 0.25788 True -ACVR2A_p148653859 ACVR2A 248.41/621.58 605.02/783.89 434.99 694.46 0.67365 42813 20943 1.7929 0.96346 0.036544 0.073087 0.25892 True -ADPRHL2_m36554557 ADPRHL2 1597.7/1046 759.59/1043.1 1321.9 901.34 -0.55195 96190 55045 1.7925 0.036525 0.96347 0.07305 0.25892 False -ADD1_p2877736 ADD1 1103.9/809.83 769.69/554.44 956.85 662.06 -0.53065 33197 27105 1.7906 0.036682 0.96332 0.073364 0.25898 False -ACTRT3_m169487273 ACTRT3 806.96/721.03 177.91/685.47 764 431.69 -0.82211 66251 34569 1.7875 0.036926 0.96307 0.073853 0.25926 False -ACVR2A_p148653946 ACVR2A 952.48/467.07 919.21/1190.1 709.78 1054.7 0.5707 77255 37255 1.7869 0.96302 0.036982 0.073964 0.25926 True -ADAD1_p123301335 ADAD1 790.79/408.47 375.38/290.67 599.63 333.02 -0.84652 38337 22314 1.7851 0.037122 0.96288 0.074244 0.25933 False -ABHD14B_p52004061 ABHD14B 389.52/605.6 377.27/185.39 497.56 281.33 -0.82038 20878 14707 1.7832 0.037275 0.96273 0.074549 0.25949 False -ACVR1_p158637068 ACVR1 689.37/1438.5 1443.5/1606.1 1063.9 1524.8 0.51878 1.4692e+05 67022 1.7801 0.96247 0.037528 0.075055 0.26035 True -ACVR2B_m38495825 ACVR2B 2063.7/2365.6 1987.3/1531.7 2214.6 1759.5 -0.33172 74667 65705 1.7755 0.037908 0.96209 0.075815 0.26117 False -ABCC1_m16043636 ABCC1 70.554/369.4 355.19/467.47 219.98 411.33 0.89992 25479 11618 1.7753 0.96128 0.038724 0.077448 0.26406 True -ABCF1_p30545638 ABCF1 213.13/376.5 404.4/482.92 294.82 443.66 0.588 8213.8 7066.6 1.7706 0.96168 0.03832 0.076639 0.263 True -ADIRF_p88728315 ADIRF 702.6/1010.5 1291.4/1017.3 856.56 1154.4 0.43007 42485 28341 1.7691 0.96156 0.038437 0.076873 0.263 True -ACHE_m100491721 ACHE 1075.9/873.77 824.57/567.03 974.86 695.8 -0.48592 26801 25309 1.7541 0.039707 0.96029 0.079415 0.26985 False -AEBP1_p44144347 AEBP1 1039.2/994.53 685.78/786.75 1016.9 736.26 -0.46529 3047.7 25743 1.7489 0.040157 0.95984 0.080313 0.27198 False -ABCB8_p150725600 ABCB8 438.02/399.59 220.81/275.79 418.8 248.3 -0.75183 1125 9594.5 1.7408 0.04086 0.95914 0.081719 0.2758 False -ADAD1_p123301358 ADAD1 1749.1/1907.4 2041.6/2387.1 1828.3 2214.3 0.27628 36115 49457 1.7361 0.95873 0.041273 0.082545 0.27703 True -ACVR1B_m52369237 ACVR1B 1472.8/1536.2 1218.2/1098.6 1504.5 1158.4 -0.37684 4584 39812 1.7345 0.041414 0.95859 0.082828 0.27703 False -AGBL5_m27276006 AGBL5 1133.3/1394.1 639.09/1131.2 1263.7 885.15 -0.51317 77554 47709 1.7331 0.04154 0.95846 0.083081 0.27703 False -AGTPBP1_p88296203 AGTPBP1 438.02/340.98 458.66/679.75 389.5 569.2 0.54615 14575 10759 1.7325 0.9584 0.041596 0.083193 0.27703 True -AGPAT3_m45379578 AGPAT3 101.42/156.28 206.93/226.01 128.85 216.47 0.74396 843.48 2586.8 1.7228 0.95729 0.042708 0.085416 0.28166 True -ADK_m75960552 ADK 1472.8/1829.2 1263/1315.4 1651 1289.2 -0.35659 32444 44151 1.7218 0.042557 0.95744 0.085114 0.28166 False -AFF2_m147967463 AFF2 604.12/911.06 929.93/1103.7 757.59 1016.8 0.42411 31106 22737 1.7193 0.95722 0.042784 0.085567 0.28166 True -ACTR3_p114691883 ACTR3 542.38/776.09 418.28/466.9 659.24 442.59 -0.57375 14246 15893 1.7185 0.042856 0.95714 0.085712 0.28166 False -AHRR_p344036 AHRR 338.07/607.37 378.53/65.229 472.72 221.88 -1.0878 42671 21542 1.7157 0.043111 0.95689 0.086221 0.28174 False -AFF3_m100623802 AFF3 626.17/191.8 130.59/185.39 408.98 157.99 -1.3666 47918 22202 1.7153 0.043149 0.95685 0.086298 0.28174 False -ADRB2_p148206408 ADRB2 1074.5/754.78 1548.8/1021.3 914.63 1285.1 0.49016 95113 46958 1.7096 0.95633 0.043672 0.087344 0.28422 True -AAAS_p53714367 AAAS 474.77/582.51 411.34/268.35 528.64 339.85 -0.63589 8013.4 12432 1.6932 0.045206 0.95479 0.090411 0.29049 False -ADIRF_p88729975 ADIRF 89.662/209.56 208.82/304.4 149.61 256.61 0.77437 5877.7 3995.3 1.6928 0.95435 0.045653 0.091307 0.29049 True -AAK1_p69870070 AAK1 1456.6/1275.1 1129.3/962.41 1365.9 1045.9 -0.38484 15199 35751 1.6926 0.045266 0.95473 0.090532 0.29049 False -AGAP2_m58129187 AGAP2 917.2/768.99 1056.7/1118 843.09 1087.4 0.36672 6431.3 20897 1.69 0.95448 0.045519 0.091037 0.29049 True -ACP1_p272054 ACP1 645.27/429.78 154.57/448.59 537.53 301.58 -0.8317 33222 19517 1.6895 0.045561 0.95444 0.091122 0.29049 False -ACTR1A_p104250304 ACTR1A 654.09/1040.7 1092.1/1155.8 847.4 1123.9 0.40703 38383 26805 1.6891 0.9544 0.045603 0.091206 0.29049 True -ACD_p67694355 ACD 752.57/795.62 1094.6/917.21 774.1 1005.9 0.37747 8329.9 19004 1.6815 0.95367 0.046333 0.092665 0.29388 True -ADRA1B_m159343996 ADRA1B 748.16/523.9 919.84/777.02 636.03 848.43 0.41512 17672 16072 1.6754 0.95307 0.046933 0.093865 0.296 True -ABI1_m27149710 ABI1 636.45/1191.7 722.37/328.43 914.06 525.4 -0.7977 1.1586e+05 53863 1.675 0.046963 0.95304 0.093926 0.296 False -AES_m3061215 AES 523.27/905.73 1206.3/844.54 714.5 1025.4 0.52056 69279 34682 1.6694 0.95248 0.04752 0.09504 0.29857 True -ADARB2_m1779251 ADARB2 742.28/598.49 943.81/820.51 670.39 882.16 0.39552 8969.8 16193 1.6642 0.95196 0.048038 0.096076 0.30078 True -ABLIM2_m8108316 ABLIM2 291.03/307.24 154.57/173.94 299.14 164.26 -0.86092 159.5 6598.9 1.6615 0.048306 0.95169 0.096612 0.30078 False -ADPRHL2_p36554606 ADPRHL2 257.23/245.08 52.995/203.12 251.15 128.06 -0.96627 5671.6 5512.4 1.6613 0.048324 0.95168 0.096648 0.30078 False -ACLY_p40069998 ACLY 877.51/715.71 538.15/594.5 796.61 566.32 -0.49151 7339.1 19619 1.6441 0.050079 0.94992 0.10016 0.31074 False -ADAP1_m994091 ADAP1 878.98/1218.3 358.98/956.11 1048.6 657.55 -0.67254 1.1793e+05 57069 1.6372 0.050797 0.9492 0.10159 0.31351 False -ADRBK2_p26057601 ADRBK2 1456.6/1681.8 1731.2/2079.3 1569.2 1905.2 0.27975 42978 42141 1.6368 0.94916 0.050839 0.10168 0.31351 True -ACVR2B_p38518841 ACVR2B 587.95/495.49 733.09/717.52 541.72 725.3 0.42037 2197.8 12775 1.6243 0.94784 0.052157 0.10431 0.32065 True -AGPAT5_p6566262 AGPAT5 696.72/486.61 950.12/681.47 591.66 815.79 0.46276 29080 19088 1.6223 0.94763 0.052374 0.10475 0.32099 True -ADAMTS5_p28338596 ADAMTS5 840.77/571.86 373.49/573.33 706.31 473.41 -0.57622 28062 20795 1.6151 0.053144 0.94686 0.10629 0.32472 False -ACP1_m272137 ACP1 1730/722.81 789.87/605.37 1226.4 697.62 -0.81305 2.6214e+05 1.0852e+05 1.6061 0.054128 0.94587 0.10826 0.3284 False -ADRBK2_m25961078 ADRBK2 316.02/419.12 497.77/529.84 367.57 513.81 0.48208 2914.6 8298.3 1.6053 0.94578 0.054217 0.10843 0.3284 True -ACVRL1_p52306245 ACVRL1 836.36/973.22 1095.2/1197 904.79 1146.1 0.34076 7272.6 22606 1.6051 0.94576 0.05424 0.10848 0.3284 True -AGAP2_m58128454 AGAP2 1212.6/1152.6 897.76/908.62 1182.6 903.19 -0.3885 931.14 30454 1.6012 0.054667 0.94533 0.10933 0.32994 False -ADRBK1_p67034160 ADRBK1 1018.6/717.48 1070.6/1144.4 868.05 1107.5 0.35108 24030 22402 1.5998 0.94517 0.054825 0.10965 0.32994 True -ACVR2B_p38518817 ACVR2B 1265.6/1760 1651/2271.6 1512.8 1961.3 0.37441 1.5737e+05 79160 1.5942 0.94456 0.055445 0.11089 0.3319 True -AGAP3_p150783857 AGAP3 1328.8/1038.9 1346.3/1596.4 1183.8 1471.4 0.31342 36634 32538 1.5939 0.94452 0.055483 0.11097 0.3319 True -ACTN4_p39138459 ACTN4 1575.7/2017.5 1196.8/1602.1 1796.6 1399.5 -0.36017 89860 62290 1.5912 0.05578 0.94422 0.11156 0.33208 False -ADPRHL2_m36554512 ADPRHL2 495.35/809.83 791.77/982.44 652.59 887.1 0.44234 33814 21748 1.5902 0.94411 0.055895 0.11179 0.33208 True -AFF2_p147967406 AFF2 1043.6/1051.4 1372.8/1240.5 1047.5 1306.7 0.31867 4392.6 26607 1.5888 0.94395 0.056048 0.1121 0.33208 True -ACTRT3_m169487247 ACTRT3 432.14/834.7 288.32/464.61 633.42 376.46 -0.7491 48282 26229 1.587 0.056259 0.94374 0.11252 0.33208 False -ACVR1B_p52369196 ACVR1B 405.68/284.15 173.49/237.46 344.92 205.47 -0.74446 4715.3 7731.4 1.5862 0.056343 0.94366 0.11269 0.33208 False -AFF3_m100625275 AFF3 521.8/237.98 606.92/516.11 379.89 561.51 0.56251 22201 13139 1.5845 0.94343 0.05657 0.11314 0.33243 True -AFMID_p76183473 AFMID 383.64/428 833.41/426.85 405.82 630.13 0.63354 41814 20114 1.5816 0.94301 0.056994 0.11399 0.33371 True -ADARB1_m46595779 ADARB1 665.85/174.04 191.79/144.76 419.95 168.28 -1.3143 61022 26756 1.5794 0.057122 0.94288 0.11424 0.33371 False -AEN_m89169460 AEN 1606.6/1554 1376.6/1138.1 1580.3 1257.3 -0.32956 14917 42050 1.5748 0.057652 0.94235 0.1153 0.33542 False -ACVR1_p158655928 ACVR1 288.09/307.24 536.89/335.87 297.67 436.38 0.55035 10194 7773.1 1.5733 0.94216 0.057844 0.11569 0.33542 True -ACTN1_m69392382 ACTN1 684.96/914.61 846.65/1418.4 799.79 1132.5 0.50135 94919 44777 1.5725 0.94208 0.057918 0.11584 0.33542 True -ADARB2_p1779233 ADARB2 442.43/229.1 395.57/651.14 335.76 523.36 0.63881 27707 14238 1.5721 0.9419 0.058104 0.11621 0.33552 True -AFF3_m100625319 AFF3 742.28/781.42 955.8/996.17 761.85 975.98 0.35693 790.26 18669 1.5672 0.94146 0.058537 0.11707 0.33705 True -ADPRHL2_m36554576 ADPRHL2 1099.5/1115.3 1309.1/1431.6 1107.4 1370.3 0.30714 3814.3 28306 1.563 0.94098 0.059024 0.11805 0.33855 True -ADCK3_m227149096 ADCK3 270.46/415.57 434.05/525.83 343.01 479.94 0.4834 7370.6 7684 1.5621 0.94086 0.059137 0.11827 0.33855 True -ACVR2B_p38495805 ACVR2B 804.02/776.09 550.14/595.07 790.05 572.6 -0.46373 699.76 19440 1.5596 0.059426 0.94057 0.11885 0.33924 False -ACSS2_p33501184 ACSS2 698.19/531.01 504.08/347.89 614.6 425.98 -0.52781 13087 14701 1.5556 0.059898 0.9401 0.1198 0.34096 False -AAAS_m53715169 AAAS 956.89/1113.5 502.82/966.99 1035.2 734.9 -0.49372 59996 37506 1.5506 0.060496 0.9395 0.12099 0.34299 False -AATK_p79104850 AATK 438.02/561.2 319.86/344.45 499.61 332.16 -0.58749 3944.4 11675 1.5498 0.060597 0.9394 0.12119 0.34299 False -ABCC1_m16043645 ABCC1 1609.5/978.55 807.54/1025.9 1294 916.73 -0.49684 1.1145e+05 59593 1.5456 0.061105 0.9389 0.12221 0.34488 False -ACTL6A_m179287986 ACTL6A 661.44/358.74 296.52/337.59 510.09 317.05 -0.68432 23329 15741 1.5388 0.061929 0.93807 0.12386 0.3477 False -ADRA1B_p159343912 ADRA1B 1149.4/1465.2 960.21/1086.6 1307.3 1023.4 -0.35292 28911 34048 1.5386 0.061952 0.93805 0.1239 0.3477 False -AGAP3_p150784015 AGAP3 486.53/673.08 784.83/734.68 579.81 759.75 0.38937 9329.5 13778 1.533 0.93737 0.062632 0.12526 0.34996 True -ACTR3_p114684915 ACTR3 895.15/824.04 700.29/571.04 859.6 635.66 -0.43481 5440.7 21353 1.5325 0.062705 0.93729 0.12541 0.34996 False -ADAM10_m59009777 ADAM10 539.44/1021.2 1090.8/1021.3 780.31 1056.1 0.43612 59222 32523 1.5292 0.93689 0.063113 0.12623 0.35125 True -ACAT2_p160183152 ACAT2 2185.7/1877.2 2122.9/2834.6 2031.4 2478.8 0.28699 1.5041e+05 87211 1.5148 0.93508 0.064918 0.12984 0.36029 True -ABL2_m179100447 ABL2 1425.8/1026.5 1555.1/1463.6 1226.1 1509.4 0.29963 41949 35119 1.5115 0.93467 0.065331 0.13066 0.36158 True -ACVR1C_m158443700 ACVR1C 992.16/637.57 876.94/1399.6 814.86 1138.2 0.48168 99717 46653 1.4972 0.93282 0.067177 0.13435 0.37018 True -ACAT2_m160183968 ACAT2 1816.8/2846.8 1813.8/1763.5 2331.8 1788.6 -0.3824 2.659e+05 1.3186e+05 1.4958 0.067352 0.93265 0.1347 0.37018 False -ACIN1_m23538815 ACIN1 1308.2/2186.2 1482.6/997.31 1747.2 1240 -0.49441 2.516e+05 1.1521e+05 1.4944 0.06754 0.93246 0.13508 0.37018 False -AGPAT3_p45379552 AGPAT3 1043.6/1156.1 1405.6/1294.8 1099.9 1350.2 0.29563 6233.8 28093 1.4937 0.93237 0.067625 0.13525 0.37018 True -ABTB1_m127395233 ABTB1 598.24/546.99 482.63/314.7 572.61 398.67 -0.52129 7706.7 13588 1.4923 0.067814 0.93219 0.13563 0.3702 False -ACLY_m40070029 ACLY 132.29/241.53 123.02/65.801 186.91 94.412 -0.97779 3802 3911.5 1.4888 0.068272 0.93173 0.13654 0.37168 False -ADARB1_m46591572 ADARB1 205.78/186.47 225.86/359.9 196.13 292.88 0.57609 4585.1 4279.5 1.479 0.93033 0.069666 0.13933 0.37824 True -AHCTF1_m247068945 AHCTF1 677.61/316.12 613.86/819.36 496.86 716.61 0.52745 43227 22145 1.4767 0.93009 0.069912 0.13982 0.37855 True -AFF2_m147924923 AFF2 601.18/328.55 370.96/181.95 464.86 276.46 -0.74764 27513 16354 1.4742 0.070213 0.92979 0.14043 0.37915 False -ADCK1_p78285402 ADCK1 1246.5/753 1301.5/1264.5 999.73 1283 0.35962 61215 37246 1.4679 0.92894 0.071062 0.14212 0.3827 True -ADIRF_p88729942 ADIRF 951.01/864.89 606.92/769.01 907.95 687.96 -0.39977 8422.9 22694 1.4603 0.072108 0.92789 0.14422 0.38729 False -ACVR1B_p52369243 ACVR1B 692.31/749.45 607.55/448.02 720.88 527.78 -0.44908 7178.6 17556 1.4574 0.072508 0.92749 0.14502 0.38839 False -ACAD9_m128598631 ACAD9 399.8/609.15 281.38/399.95 504.48 340.67 -0.56506 14472 12692 1.4541 0.072959 0.92704 0.14592 0.38977 False -ADRA1A_p26722438 ADRA1A 1093.6/934.15 726.79/835.96 1013.9 781.37 -0.37536 9334.6 25659 1.4514 0.07333 0.92667 0.14666 0.3907 False -AFTPH_p64778713 AFTPH 1731.5/1507.8 1543.8/999.6 1619.6 1271.7 -0.34868 86548 57661 1.449 0.073666 0.92633 0.14733 0.39145 False -ADCK2_m140373155 ADCK2 77.903/97.677 99.681/212.28 87.79 155.98 0.82211 3267.4 2214.8 1.4489 0.92396 0.076041 0.15208 0.39793 True -ADI1_m3523162 ADI1 592.36/710.38 362.76/578.48 651.37 470.62 -0.46807 15115 15683 1.4433 0.074462 0.92554 0.14892 0.39463 False -ADCY1_p45614127 ADCY1 1725.6/2271.4 1786.7/1442.5 1998.5 1614.6 -0.30762 1.041e+05 71107 1.4399 0.074949 0.92505 0.1499 0.39616 False -AFMID_p76187082 AFMID 1137.7/1028.3 803.75/885.74 1083 844.75 -0.35804 4672.8 27613 1.4337 0.075835 0.92417 0.15167 0.39793 False -AFF1_m87967432 AFF1 173.44/602.05 147.63/215.14 387.75 181.38 -1.0918 47064 21559 1.4316 0.076124 0.92388 0.15225 0.39793 False -ABHD14B_p52004011 ABHD14B 501.23/635.79 668.74/800.48 568.51 734.61 0.36923 8865.6 13480 1.4307 0.92374 0.07626 0.15252 0.39793 True -ABT1_p26597361 ABT1 339.54/447.54 310.4/205.99 393.54 258.19 -0.60615 5641.4 8952.9 1.4305 0.07628 0.92372 0.15256 0.39793 False -ACAD11_p132378566 ACAD11 679.08/777.87 449.82/627.11 728.47 538.47 -0.43532 10297 17761 1.4257 0.076978 0.92302 0.15396 0.40053 False -ACTR5_m37377189 ACTR5 679.08/774.31 1578.5/680.32 726.7 1129.4 0.63543 2.0394e+05 79789 1.4257 0.92263 0.077373 0.15475 0.40069 True -ADARB2_m1779337 ADARB2 665.85/701.5 986.71/746.7 683.68 866.7 0.34178 14719 16550 1.4227 0.92259 0.077411 0.15482 0.40069 True -ACVR1C_m158401030 ACVR1C 1099.5/1435 1341.3/1834.4 1267.2 1587.8 0.32518 88938 51572 1.4119 0.92101 0.078993 0.15799 0.40783 True -ADH7_p100349698 ADH7 1087.7/847.13 994.28/1551.8 967.42 1273 0.39569 92164 46957 1.4103 0.92077 0.079228 0.15846 0.40798 True -ADRA1B_p159343905 ADRA1B 410.09/293.03 44.162/334.73 351.56 189.44 -0.88851 24533 13442 1.4058 0.079898 0.9201 0.1598 0.41038 False -ADARB2_p1421336 ADARB2 561.49/543.44 881.98/580.76 552.47 731.37 0.40409 22765 16293 1.4016 0.91949 0.080514 0.16103 0.412 True -ABTB1_p127395220 ABTB1 652.62/1113.5 814.48/296.39 883.07 555.43 -0.66795 1.2021e+05 54739 1.4009 0.080627 0.91937 0.16125 0.412 False -ADRA1B_m159343926 ADRA1B 526.21/820.49 1157.1/708.36 673.35 932.71 0.46947 71980 34842 1.3895 0.91764 0.08236 0.16472 0.41884 True -AEBP1_p44144306 AEBP1 971.58/944.8 922.99/509.81 958.19 716.4 -0.41904 42858 30350 1.3879 0.082582 0.91742 0.16516 0.41884 False -ADCK1_m78285358 ADCK1 1547.8/1966 1870/2354 1756.9 2112 0.26543 1.0229e+05 65637 1.386 0.91712 0.082878 0.16576 0.41884 True -AATK_m79102275 AATK 912.79/772.54 2881.9/634.55 842.66 1758.2 1.0602 1.2676e+06 4.3645e+05 1.3859 0.90779 0.092212 0.18442 0.44502 True -ABI1_p27112174 ABI1 1484.6/1314.2 2124.2/1406.4 1399.4 1765.3 0.33492 1.3606e+05 69839 1.3847 0.91692 0.083076 0.16615 0.41884 True -ACO2_p41895716 ACO2 302.79/348.09 404.4/481.78 325.44 443.09 0.44403 2009.6 7247.4 1.382 0.9165 0.083496 0.16699 0.41884 True -ACVR1C_m158401075 ACVR1C 283.69/312.57 297.15/608.23 298.13 452.69 0.60095 24401 12516 1.3815 0.91612 0.083879 0.16776 0.41884 True -AEBP2_m19615578 AEBP2 348.36/671.31 820.79/597.36 509.83 709.07 0.47511 38554 20812 1.3811 0.91635 0.083646 0.16729 0.41884 True -AGFG1_m228337201 AGFG1 933.37/880.87 635.31/763.29 907.12 699.3 -0.37491 4784 22671 1.3802 0.083756 0.91624 0.16751 0.41884 False -ACTL7A_p111624607 ACTL7A 346.89/715.71 558.34/1060.8 531.3 809.58 0.60672 97130 40711 1.3792 0.91573 0.084272 0.16854 0.41884 True -AFF4_p132272853 AFF4 756.98/676.64 883.25/914.35 716.81 898.8 0.326 1855.7 17445 1.3778 0.91587 0.084127 0.16825 0.41884 True -ACIN1_m23535193 ACIN1 561.49/824.04 428.37/579.05 692.77 503.71 -0.45899 22909 18833 1.3776 0.084162 0.91584 0.16832 0.41884 False -ADARB2_m1779308 ADARB2 1140.6/1191.7 880.09/977.86 1166.1 928.97 -0.32771 3040.9 29983 1.3697 0.085394 0.91461 0.17079 0.42337 False -ADCK5_m145603137 ADCK5 1195/1083.3 659.91/1098 1139.2 878.96 -0.37373 51102 36508 1.3618 0.086629 0.91337 0.17326 0.42843 False -ADH5_m100003254 ADH5 1178.8/1243.2 868.1/1075.1 1211 971.62 -0.31745 11749 31269 1.3537 0.087909 0.91209 0.17582 0.43368 False -ADARB2_p1421307 ADARB2 418.91/300.14 376.64/645.42 359.52 511.03 0.50613 21588 12593 1.3501 0.91144 0.088556 0.17711 0.4358 True -AEN_m89169544 AEN 536.5/687.29 504.71/394.23 611.9 449.47 -0.44421 8735.7 14629 1.3429 0.089651 0.91035 0.1793 0.43999 False -ACIN1_p23538745 ACIN1 568.84/612.7 564.02/270.64 590.77 417.33 -0.5004 21998 16711 1.3417 0.089848 0.91015 0.1797 0.43999 False -ADCK2_m140373220 ADCK2 345.42/582.51 849.18/484.07 463.97 666.62 0.52191 47380 22961 1.3374 0.90935 0.090646 0.18129 0.44173 True -AHCY_m32883301 AHCY 392.46/412.02 334.37/214 402.24 274.18 -0.55124 3718.3 9173.3 1.3371 0.090599 0.9094 0.1812 0.44173 False -ADI1_m3517677 ADI1 414.5/488.39 226.49/403.96 451.44 315.22 -0.51679 9238.6 10430 1.3339 0.091125 0.90887 0.18225 0.44261 False -ACTL7A_p111624699 ACTL7A 1214.1/776.09 893.97/543.57 995.1 718.77 -0.46875 78661 42974 1.333 0.091269 0.90873 0.18254 0.44261 False -ADRBK1_p67034261 ADRBK1 680.55/634.01 318.6/631.12 657.28 474.86 -0.46818 24958 18880 1.3276 0.092148 0.90785 0.1843 0.44502 False -ADAM10_m59009883 ADAM10 1197.9/1882.5 1693.9/2150.3 1540.2 1922.1 0.31936 1.6921e+05 83648 1.3204 0.90664 0.093357 0.18671 0.44946 True -ADNP_m49518579 ADNP 1805/1435 1712.2/2152.5 1620 1932.4 0.25426 82700 56385 1.3156 0.90585 0.094147 0.18829 0.45218 True -AGL_m100327186 AGL 138.17/159.84 400.62/132.75 149 266.68 0.83553 18056 8045.4 1.312 0.90042 0.099578 0.19916 0.46422 True -AGL_p100327074 AGL 607.06/985.65 651.08/514.39 796.35 582.73 -0.44991 40504 26576 1.3104 0.095036 0.90496 0.19007 0.45433 False -AFF4_p132272762 AFF4 561.49/474.18 718.58/605.94 517.83 662.26 0.3543 5078 12150 1.3103 0.90495 0.09505 0.1901 0.45433 True -ACP1_p264954 ACP1 1267/1397.7 929.3/1247.4 1332.3 1088.3 -0.29162 29557 34775 1.3086 0.095342 0.90466 0.19068 0.45464 False -ABL1_m133729532 ABL1 1400.8/1465.2 2174.7/1405.3 1433 1790 0.32073 1.4903e+05 74817 1.3052 0.90409 0.095914 0.19183 0.45628 True -ADRM1_p60878642 ADRM1 94.072/124.32 70.66/451.45 109.19 261.06 1.2498 36479 13594 1.3025 0.88325 0.11675 0.2335 0.50165 True -AEBP2_p19615498 AEBP2 936.31/907.51 624.58/824.51 921.91 724.55 -0.34712 10201 23083 1.299 0.096966 0.90303 0.19393 0.45921 False -AFMID_p76183450 AFMID 798.14/1559.3 606.92/1007.6 1178.7 807.26 -0.54554 1.8497e+05 81886 1.2982 0.097118 0.90288 0.19424 0.45921 False -AFF2_m147891420 AFF2 505.64/122.54 115.45/183.1 314.09 149.28 -1.0681 37834 17256 1.2976 0.09722 0.90278 0.19444 0.45921 False -AGPAT5_m6566307 AGPAT5 288.09/250.41 232.8/107.57 269.25 170.18 -0.65875 4275.6 5869.9 1.2942 0.097799 0.9022 0.1956 0.46018 False -ABLIM2_m8160405 ABLIM2 1336.1/1784.8 1398.7/1144.9 1560.5 1271.8 -0.29489 66433 49787 1.2937 0.097886 0.90211 0.19577 0.46018 False -ABL1_m133729488 ABL1 621.76/651.77 688.3/35.475 636.76 361.89 -0.8135 1.0677e+05 45785 1.2922 0.098144 0.90186 0.19629 0.46031 False -ACVRL1_m52306921 ACVRL1 740.81/525.68 1467.4/548.15 633.25 1007.8 0.66952 2.2285e+05 84415 1.2891 0.89986 0.10014 0.20028 0.46422 True -ADCK4_p41220422 ADCK4 161.69/333.88 275.07/465.18 247.78 370.13 0.57703 16449 9050.7 1.286 0.90032 0.09968 0.19936 0.46422 True -AHNAK2_m105423809 AHNAK2 404.21/470.63 489.57/643.13 437.42 566.35 0.37193 6997.9 10070 1.2848 0.90057 0.099429 0.19886 0.46422 True -AATK_p79102286 AATK 1887.3/998.08 1163.4/929.22 1442.7 1046.3 -0.46311 2.1139e+05 95793 1.2808 0.10013 0.89987 0.20027 0.46422 False -ADIRF_p88729951 ADIRF 435.08/435.11 275.7/339.88 435.09 307.79 -0.49803 1029.7 10011 1.2724 0.10161 0.89839 0.20322 0.46994 False -ADAR_m154574219 ADAR 634.98/1269.8 1618.9/1005.9 952.39 1312.4 0.46214 1.9468e+05 80849 1.266 0.89721 0.10279 0.20558 0.47431 True -ADH5_p100002504 ADH5 668.79/717.48 772.84/938.95 693.14 855.89 0.3039 7490.8 16805 1.2555 0.89535 0.10465 0.2093 0.48177 True -AHNAK2_m105444541 AHNAK2 761.39/664.2 846.65/909.2 712.8 877.93 0.30022 3339.3 17337 1.2541 0.8951 0.1049 0.20981 0.48183 True -ABCB8_p150730680 ABCB8 773.15/854.23 638.46/633.98 813.69 636.22 -0.35447 1648.5 20088 1.2522 0.10525 0.89475 0.21051 0.48234 False -ACVR1B_m52369192 ACVR1B 632.04/475.95 1491.4/427.99 554 959.71 0.79161 2.8881e+05 1.05e+05 1.252 0.88991 0.11009 0.22017 0.49176 True -ADRB3_m37823867 ADRB3 251.35/237.98 251.09/438.86 244.66 344.98 0.49401 8859 6470.9 1.2471 0.89369 0.10631 0.21263 0.48496 True -ACTL6A_p179287911 ACTL6A 1281.7/1189.9 1228.3/725.53 1235.8 976.93 -0.33881 65315 43093 1.247 0.10619 0.89381 0.21238 0.48496 False -AFF2_m147924918 AFF2 858.4/1136.6 795.55/2439.8 997.51 1617.7 0.69696 6.9522e+05 2.4854e+05 1.244 0.89076 0.10924 0.21847 0.49176 True -ADIRF_m88728350 ADIRF 244/239.75 130.59/174.52 241.88 152.55 -0.66147 486.77 5210.1 1.2394 0.1076 0.8924 0.2152 0.48865 False -ADIRF_m88728302 ADIRF 492.41/221.99 309.77/102.99 357.2 206.38 -0.78849 28970 15015 1.2393 0.10761 0.89239 0.21522 0.48865 False -ACTL6A_p179287902 ACTL6A 754.04/884.42 623.32/663.16 819.23 643.24 -0.34844 4646.4 20240 1.2371 0.10803 0.89197 0.21607 0.48946 False -AFF4_p132272771 AFF4 551.2/1387 1147/1512.3 969.11 1329.6 0.45588 2.0801e+05 85604 1.2322 0.891 0.109 0.21799 0.49176 True -ADH5_m100006354 ADH5 1427.2/1157.9 1642.8/1393.3 1292.6 1518 0.2318 33706 33650 1.2291 0.89048 0.10952 0.21903 0.49176 True -AHNAK_m62303528 AHNAK 266.05/797.4 677.58/839.39 531.72 758.48 0.51163 77130 34052 1.2288 0.89022 0.10978 0.21957 0.49176 True -AGAP3_p150783972 AGAP3 366/369.4 333.74/746.7 367.7 540.22 0.55377 42636 19746 1.2277 0.88973 0.11027 0.22053 0.49176 True -AFAP1L2_p116092995 AFAP1L2 1383.1/864.89 894.6/851.98 1124 873.29 -0.36376 67603 41721 1.2275 0.10982 0.89018 0.21963 0.49176 False -ADIRF_m88729968 ADIRF 734.94/673.08 620.8/470.91 704.01 545.85 -0.3665 6573.2 17099 1.2095 0.11323 0.88677 0.22647 0.50165 False -ACSL6_p131329816 ACSL6 1797.7/1074.4 1233.4/983.58 1436.1 1108.5 -0.37322 1.4636e+05 73986 1.2043 0.11424 0.88576 0.22849 0.50165 False -ABTB1_m127395855 ABTB1 560.02/887.97 1113.5/759.28 724 936.4 0.37069 58259 31180 1.2029 0.88549 0.11451 0.22902 0.50165 True -AHCTF1_p247076572 AHCTF1 551.2/740.57 1103.4/622.53 645.89 862.98 0.41749 66780 32617 1.2021 0.88531 0.11469 0.22938 0.50165 True -ADRM1_m60878651 ADRM1 736.41/662.43 562.12/523.55 699.42 542.83 -0.36505 1740.2 16975 1.2018 0.11472 0.88528 0.22943 0.50165 False -AHRR_m344069 AHRR 415.97/372.95 212.61/349.03 394.46 280.82 -0.48876 5115.4 8976.2 1.1995 0.11516 0.88484 0.23032 0.50165 False -ACVR1B_p52369255 ACVR1B 167.57/166.94 44.162/150.48 167.25 97.323 -0.77502 2826.2 3457 1.1994 0.11518 0.88482 0.23037 0.50165 False -ADD3_p111860553 ADD3 1881.4/1378.1 1584.8/1004.2 1629.8 1294.5 -0.33207 1.4761e+05 78215 1.1989 0.11528 0.88472 0.23056 0.50165 False -ADCK3_p227149133 ADCK3 724.65/648.22 702.81/999.6 686.43 851.21 0.30998 23481 18910 1.1982 0.88458 0.11542 0.23083 0.50165 True -AGPAT5_p6566257 AGPAT5 1383.1/1310.6 1145.1/1099.2 1346.9 1122.1 -0.26321 1840.8 35198 1.1981 0.11543 0.88457 0.23086 0.50165 False -ADPRHL2_p36554501 ADPRHL2 598.24/694.4 675.68/921.21 646.32 798.45 0.30453 17383 16159 1.1968 0.8843 0.1157 0.2314 0.50165 True -ACRC_m70811980 ACRC 461.54/502.59 445.41/889.17 482.07 667.29 0.46825 49652 24031 1.1948 0.88382 0.11618 0.23237 0.50165 True -AGAP3_p150783903 AGAP3 693.78/678.41 784.2/895.46 686.1 839.83 0.2913 3154.2 16616 1.1926 0.8835 0.1165 0.23301 0.50165 True -AHCTF1_m247094617 AHCTF1 845.18/660.65 541.3/640.84 752.91 591.07 -0.34862 10989 18426 1.1923 0.11658 0.88342 0.23315 0.50165 False -AAAS_m53715212 AAAS 373.35/603.82 270.65/424.56 488.58 347.61 -0.48996 19201 13993 1.1919 0.11666 0.88334 0.23331 0.50165 False -ACTRT3_m169487192 ACTRT3 510.05/172.27 218.29/177.38 341.16 197.83 -0.7831 28942 14739 1.1916 0.1167 0.8833 0.23341 0.50165 False -ACD_m67694260 ACD 1189.1/1653.4 1320.5/977.29 1421.3 1148.9 -0.30672 83330 52689 1.1867 0.11767 0.88233 0.23534 0.50452 False -ADAMTS5_p28338620 ADAMTS5 399.8/209.56 357.08/470.33 304.68 413.71 0.44006 12254 8574.9 1.1774 0.88042 0.11958 0.23917 0.51163 True -ACVR2B_p38518810 ACVR2B 1146.5/1292.9 1507.8/1346.9 1219.7 1427.4 0.22667 11831 31519 1.1698 0.87895 0.12105 0.24209 0.51589 True -AEN_m89169478 AEN 1381.7/1330.2 1509.1/327.29 1355.9 918.19 -0.56192 3.4983e+05 1.4025e+05 1.1695 0.1211 0.8789 0.24219 0.51589 False -ADRM1_m60878634 ADRM1 410.09/657.1 435.31/356.47 533.6 395.89 -0.42971 16807 13977 1.1648 0.12205 0.87795 0.2441 0.51884 False -ABL2_p179100503 ABL2 951.01/994.53 996.17/504.09 972.77 750.13 -0.37451 61010 36673 1.1626 0.1225 0.8775 0.245 0.51965 False -ADNP2_p77875482 ADNP2 561.49/390.71 480.11/775.31 476.1 627.71 0.3981 29077 17069 1.1604 0.87704 0.12296 0.24592 0.51977 True -ACHE_m100491735 ACHE 1519.8/1601.9 1329.3/1320.6 1560.9 1324.9 -0.23626 1702.3 41476 1.1585 0.12333 0.87667 0.24666 0.51977 False -ACVR1B_p52369266 ACVR1B 1170/1205.9 1523/1258.2 1187.9 1390.6 0.22706 17843 30607 1.1584 0.87664 0.12336 0.24672 0.51977 True -AFF4_m132272826 AFF4 1155.3/2191.5 2025.8/2123.4 1673.4 2074.6 0.30985 2.7081e+05 1.2015e+05 1.1573 0.87643 0.12357 0.24714 0.51977 True -AEBP2_m19615484 AEBP2 104.36/387.16 322.38/409.68 245.76 366.03 0.57281 21899 10835 1.1555 0.87491 0.12509 0.25018 0.52376 True -AFF1_p87967353 AFF1 1785.9/1459.8 960.21/1647.3 1622.9 1303.8 -0.31564 1.446e+05 77077 1.1494 0.1252 0.8748 0.2504 0.52376 False -ADH7_p100350718 ADH7 171.97/321.45 229.01/65.229 246.71 147.12 -0.74188 12292 7648 1.1489 0.1253 0.8747 0.25061 0.52376 False -AEN_m89169484 AEN 1327.3/903.96 975.99/821.65 1115.6 898.82 -0.31144 50758 35946 1.1435 0.12641 0.87359 0.25282 0.52704 False -ADNP_m49511043 ADNP 408.62/523.9 483.89/701.49 466.26 592.69 0.34548 15160 12261 1.1418 0.87323 0.12677 0.25355 0.52704 True -ACSL6_p131329859 ACSL6 242.53/225.55 324.91/304.97 234.04 314.94 0.42676 171.47 5022.7 1.1416 0.87312 0.12688 0.25376 0.52704 True -A1CF_p52595870 A1CF 915.73/1035.4 948.23/639.13 975.55 793.68 -0.29733 27465 25543 1.138 0.12756 0.87244 0.25512 0.52877 False -AFF3_m100625326 AFF3 467.42/451.09 520.48/629.97 459.25 575.23 0.32421 3063.5 10631 1.1248 0.86966 0.13034 0.26068 0.53916 True -AGBL5_p27275870 AGBL5 684.96/546.99 394.31/566.46 615.98 480.38 -0.35803 12168 14737 1.1169 0.13201 0.86799 0.26402 0.54481 False -AGPAT3_p45379618 AGPAT3 58.795/703.28 440.99/837.1 381.04 639.05 0.74447 1.4306e+05 53446 1.116 0.86089 0.13911 0.27822 0.56377 True -ACTL6B_m100253081 ACTL6B 582.07/690.84 473.17/524.12 636.46 498.64 -0.35143 3607 15284 1.1148 0.13248 0.86752 0.26496 0.54481 False -ABL1_p133729502 ABL1 774.62/529.23 675.68/228.87 651.93 452.28 -0.52653 64964 32120 1.1145 0.13252 0.86748 0.26504 0.54481 False -AHRR_m353892 AHRR 351.3/150.96 288.95/416.55 251.13 352.75 0.48857 14105 8323.1 1.1139 0.86694 0.13306 0.26612 0.5459 True -ADAMTS5_m28338697 ADAMTS5 1068.6/749.45 541.93/875.44 909.02 708.69 -0.35872 53270 32906 1.1044 0.13471 0.86529 0.26942 0.55154 False -AGBL5_p27275898 AGBL5 1108.3/1969.5 1066.2/1333.8 1538.9 1200 -0.35863 2.0333e+05 94995 1.0997 0.13574 0.86426 0.27148 0.55463 False -ACVR2B_p38518836 ACVR2B 1465.5/777.87 744.45/951.54 1121.7 847.99 -0.4031 1.2892e+05 62115 1.0981 0.13608 0.86392 0.27217 0.55489 False -ACTR1A_p104262351 ACTR1A 973.05/1099.3 768.42/2344.2 1036.2 1556.3 0.5864 6.2477e+05 2.2578e+05 1.0947 0.86114 0.13886 0.27772 0.56377 True -AGTPBP1_p88307700 AGTPBP1 157.28/118.99 40.377/603.08 138.13 321.73 1.2139 79525 28371 1.09 0.82636 0.17364 0.34729 0.6308 True -ADRB1_p115803878 ADRB1 438.02/1149 1278.8/877.15 793.53 1078 0.4415 1.6672e+05 68596 1.0861 0.86111 0.13889 0.27778 0.56377 True -ABL1_p133729441 ABL1 630.57/417.35 495.25/280.94 523.96 388.09 -0.43209 22848 15823 1.0802 0.14003 0.85997 0.28006 0.56546 False -AFF2_m147891412 AFF2 774.62/500.82 838.45/723.24 637.72 780.84 0.2917 22061 17565 1.0799 0.85991 0.14009 0.28018 0.56546 True -ADIRF_p88729984 ADIRF 1328.8/1571.7 1123/1356.6 1450.2 1239.8 -0.226 28405 38217 1.0764 0.14088 0.85912 0.28175 0.56748 False -ACAD9_m128598607 ACAD9 133.76/214.89 205.67/272.36 174.32 239.01 0.4531 2757.4 3619.8 1.0752 0.85859 0.14141 0.28281 0.56847 True -ACHE_p100491631 ACHE 540.91/587.84 672.53/703.78 564.38 688.16 0.28562 794.73 13371 1.0705 0.8578 0.1422 0.28441 0.57053 True -ADAR_m154574098 ADAR 1572.8/1028.3 1218.2/871.43 1300.5 1044.8 -0.31553 1.0419e+05 57297 1.0681 0.14273 0.85727 0.28546 0.57108 False -ACVR2A_p148602754 ACVR2A 514.45/252.18 461.18/539.57 383.32 500.37 0.38358 18733 12041 1.0667 0.85692 0.14308 0.28615 0.57108 True -AFTPH_m64778841 AFTPH 746.69/456.42 1070/553.3 601.56 811.64 0.43153 87807 38838 1.066 0.85663 0.14337 0.28674 0.57108 True -AHNAK2_m105423822 AHNAK2 316.02/735.24 709.75/664.87 525.63 687.31 0.38627 44440 23049 1.065 0.85651 0.14349 0.28697 0.57108 True -AGBL5_p27275854 AGBL5 296.91/829.37 489.57/1233.1 563.14 861.31 0.61215 2.0907e+05 78581 1.0637 0.85298 0.14702 0.29403 0.57688 True -ADAM12_m128019019 ADAM12 1106.8/1305.3 1081.3/955.54 1206.1 1018.4 -0.24372 13808 31127 1.0634 0.14379 0.85621 0.28758 0.57116 False -AGPAT5_m6566240 AGPAT5 802.55/1594.8 872.52/950.39 1198.7 911.46 -0.39482 1.5843e+05 73420 1.06 0.14457 0.85543 0.28914 0.5713 False -ABTB1_p127395242 ABTB1 842.24/1424.3 853.59/946.96 1133.3 900.28 -0.33173 86882 48323 1.0599 0.14459 0.85541 0.28918 0.5713 False -ABT1_p26597293 ABT1 523.27/694.4 336.9/597.93 608.83 467.41 -0.38064 24355 17817 1.0595 0.14468 0.85532 0.28936 0.5713 False -ACTR8_m53916073 ACTR8 1500.7/1316 800.6/1476.2 1408.4 1138.4 -0.30675 1.2265e+05 65545 1.0544 0.14585 0.85415 0.2917 0.57477 False -AGBL5_m27276001 AGBL5 624.7/554.1 370.96/558.45 589.4 464.71 -0.34226 10034 14032 1.0526 0.14626 0.85374 0.29251 0.57523 False -AEBP2_p19615557 AEBP2 1187.7/859.56 1404.4/1045.9 1023.6 1225.2 0.25907 59027 36965 1.0483 0.85275 0.14725 0.2945 0.57688 True -ADRBK1_m67034255 ADRBK1 586.48/987.43 743.82/1312 786.95 1027.9 0.38494 1.209e+05 53204 1.0447 0.85186 0.14814 0.29627 0.57921 True -AFMID_m76183493 AFMID 446.84/191.8 220.18/208.85 319.32 214.51 -0.57174 16293 10162 1.0425 0.14858 0.85142 0.29717 0.57983 False -ADAR_p154574121 ADAR 0/8.8797 0/0 4.4399 0 -2.4436 19.712 61.451 1.0394 0.85069 0.14931 0.29862 0.5806 False -ADCK4_p41220276 ADCK4 680.55/534.56 550.77/1025.9 607.56 788.34 0.37527 61771 30266 1.0392 0.85061 0.14939 0.29879 0.5806 True -AATF_p35307505 AATF 995.1/477.73 728.05/275.22 736.42 501.63 -0.55297 1.1818e+05 51379 1.0379 0.14966 0.85034 0.29931 0.5806 False -ADRB2_p148206432 ADRB2 467.42/1118.8 592.41/537.28 793.13 564.84 -0.48898 1.0685e+05 48633 1.0358 0.15015 0.84985 0.3003 0.5814 False -AGFG1_m228337138 AGFG1 1049.5/1001.6 1732.4/890.89 1025.6 1311.7 0.35467 1.7762e+05 76532 1.0342 0.84945 0.15055 0.30109 0.58142 True -ABL2_m179100597 ABL2 621.76/765.43 919.84/735.25 693.59 827.55 0.25441 13679 16818 1.0329 0.84918 0.15082 0.30165 0.58142 True -ACTR5_p37377198 ACTR5 373.35/479.51 424.59/652.29 426.43 538.44 0.33578 15779 11786 1.0318 0.8489 0.1511 0.30219 0.58142 True -ACTR5_p37377178 ACTR5 379.23/664.2 864.32/508.1 521.72 686.21 0.39472 52027 25510 1.0299 0.84839 0.15161 0.30322 0.58142 True -ADRBK1_m67034242 ADRBK1 1462.5/1209.4 1537.5/1519.1 1336 1528.3 0.19391 16099 34881 1.0299 0.84846 0.15154 0.30308 0.58142 True -ACAD11_m132378487 ACAD11 546.79/717.48 826.47/690.62 632.14 758.54 0.26262 11897 15168 1.0264 0.84764 0.15236 0.30472 0.58317 True -AFF1_m87967920 AFF1 751.1/1630.3 1093.3/632.83 1190.7 863.08 -0.46379 2.4627e+05 1.0255e+05 1.0235 0.15304 0.84696 0.30609 0.58416 False -AHCY_p32883227 AHCY 1071.5/406.69 645.4/349.6 739.11 497.5 -0.57015 1.3238e+05 56160 1.0228 0.1532 0.8468 0.30641 0.58416 False -ABTB1_m127395403 ABTB1 846.65/1557.5 482.63/1248.5 1202.1 865.57 -0.47335 2.7297e+05 1.1166e+05 1.0076 0.15683 0.84317 0.31365 0.59683 False -ACAT2_p160183940 ACAT2 22.048/0 25.866/22.315 11.024 24.091 1.0612 124.68 168.55 1.0065 0.80415 0.19585 0.3917 0.661 True -ADCK4_m41220465 ADCK4 812.84/845.35 946.33/999.6 829.09 972.97 0.2306 973.58 20512 1.0046 0.84245 0.15755 0.31511 0.59815 True -ACP1_p272032 ACP1 1616.9/1427.9 1369/1272.5 1522.4 1320.8 -0.20477 11258 40338 1.0037 0.15777 0.84223 0.31554 0.59815 False -ABHD14B_p52004005 ABHD14B 186.67/415.57 333.74/472.62 301.12 403.18 0.41987 17921 10405 1.0005 0.84122 0.15878 0.31756 0.60085 True -ADAM10_m59041696 ADAM10 499.76/458.19 409.45/338.73 478.98 374.09 -0.35573 1682 11140 0.99376 0.16017 0.83983 0.32034 0.6044 False -ADRM1_p60878730 ADRM1 621.76/635.79 392.41/621.39 628.77 506.9 -0.31028 13157 15078 0.99248 0.16048 0.83952 0.32096 0.6044 False -ACVR2B_m38518849 ACVR2B 646.74/737.02 715.43/378.78 691.88 547.11 -0.33815 30370 21304 0.99188 0.16063 0.83937 0.32126 0.6044 False -ACSL6_m131326630 ACSL6 1031.8/605.6 802.49/1259.4 818.72 1030.9 0.33214 97607 46020 0.98921 0.83871 0.16129 0.32258 0.60575 True -AES_m3056337 AES 827.54/1225.4 1143.2/1261.1 1026.5 1202.1 0.2277 43050 31693 0.98671 0.83811 0.16189 0.32378 0.60652 True -ACVRL1_m52306910 ACVRL1 1275.8/1136.6 1464.9/1295.4 1206.2 1380.2 0.1942 12030 31132 0.98585 0.8379 0.1621 0.32421 0.60652 True -ADCK4_m41220291 ADCK4 224.89/188.25 700.29/81.25 206.57 390.77 0.9164 96138 34960 0.98514 0.81248 0.18752 0.37505 0.64631 True -AAAS_p53714374 AAAS 430.67/463.52 540.04/553.87 447.1 546.96 0.29025 317.6 10318 0.98307 0.83721 0.16279 0.32557 0.60794 True -ACTN4_p39138433 ACTN4 867.22/840.02 1026.5/966.41 853.62 996.44 0.22294 1086.3 21188 0.98111 0.83673 0.16327 0.32654 0.6086 True -ACVR2A_p148653965 ACVR2A 573.25/168.72 119.87/333.58 370.98 226.73 -0.70794 52330 23033 0.97466 0.16486 0.83514 0.32973 0.61302 False -ACTL6B_p100253446 ACTL6B 1209.7/827.59 1085.1/472.62 1018.6 778.88 -0.38675 1.3029e+05 60627 0.97385 0.16507 0.83493 0.33013 0.61302 False -AGPAT3_m45379590 AGPAT3 1203.8/1630.3 1392.4/997.88 1417.1 1195.1 -0.24556 84380 52957 0.96445 0.16741 0.83259 0.33482 0.62057 False -ABLIM2_p8108320 ABLIM2 149.93/67.486 97.788/234.59 108.71 166.19 0.60785 6378.1 3553.6 0.96432 0.82664 0.17336 0.34671 0.6308 True -ABCB8_m150730720 ABCB8 146.99/266.39 292.73/247.75 206.69 270.24 0.38516 4070.2 4374.5 0.9609 0.83155 0.16845 0.3369 0.62326 True -ACVR2A_m148653941 ACVR2A 582.07/653.55 470.64/1227.3 617.81 848.99 0.45795 1.4442e+05 57998 0.95993 0.83058 0.16942 0.33884 0.62453 True -ABT1_p26597240 ABT1 119.06/378.28 185.48/128.74 248.67 157.11 -0.65907 17603 9449.8 0.95907 0.16876 0.83124 0.33752 0.62326 False -ADRBK2_p25961098 ADRBK2 2034.3/822.26 1083.2/1041.9 1428.3 1062.6 -0.42635 3.6769e+05 1.4761e+05 0.95215 0.17051 0.82949 0.34102 0.62738 False -ACAT2_m160183956 ACAT2 796.67/1024.7 1444.1/813.64 910.7 1128.9 0.30954 1.1237e+05 52638 0.95096 0.82918 0.17082 0.34164 0.62738 True -AATK_p79102311 AATK 107.3/326.77 531.84/160.78 217.04 346.31 0.67165 46463 18567 0.94873 0.81853 0.18147 0.36294 0.6361 True -ABCB8_m150725669 ABCB8 1206.8/754.78 952.64/616.24 980.77 784.44 -0.32188 79365 42941 0.94744 0.17171 0.82829 0.34341 0.62949 False -ABCC1_m16101788 ABCC1 665.85/525.68 475.69/490.36 595.77 483.03 -0.30208 4965.8 14200 0.94608 0.17205 0.82795 0.34411 0.6296 False -ADAMTS5_p28338524 ADAMTS5 902.5/1040.7 1009.4/594.5 971.6 801.96 -0.27652 47816 32253 0.94461 0.17243 0.82757 0.34486 0.6296 False -AGPAT5_p6566182 AGPAT5 483.59/413.8 498.4/591.06 448.69 544.73 0.27926 3364.2 10359 0.94362 0.82732 0.17268 0.34537 0.6296 True -ABLIM2_m8108294 ABLIM2 1042.1/2282.1 1859.2/2234.4 1662.1 2046.8 0.30019 4.1955e+05 1.6951e+05 0.93436 0.82494 0.17506 0.35013 0.631 True -ADAM12_m128018982 ADAM12 643.8/353.41 417.02/350.17 498.61 383.6 -0.37745 22199 15166 0.93401 0.17515 0.82485 0.3503 0.631 False -AGAP3_p150783866 AGAP3 352.77/234.43 310.4/429.14 293.6 369.77 0.33177 7026 6650.8 0.934 0.82482 0.17518 0.35036 0.631 True -ACTN1_m69445694 ACTN1 311.61/630.46 514.81/110.43 471.04 312.62 -0.58989 66296 29388 0.93365 0.17524 0.82476 0.35049 0.631 False -ADK_p76074475 ADK 198.43/28.415 249.83/114.44 113.42 182.13 0.67851 11810 5433.1 0.93218 0.81278 0.18722 0.37443 0.64631 True -ADAR_p154574126 ADAR 342.48/403.14 234.69/339.88 372.81 287.28 -0.37482 3685.9 8429.9 0.93159 0.17577 0.82423 0.35155 0.631 False -ADCK4_m41220243 ADCK4 830.48/864.89 1349.5/740.97 847.68 1045.2 0.3019 92864 44971 0.93153 0.8242 0.1758 0.35159 0.631 True -AGBL5_p27275820 AGBL5 423.32/412.02 224.6/426.85 417.67 325.72 -0.35775 10258 9796.5 0.92903 0.17644 0.82356 0.35287 0.631 False -AGAP2_m58128438 AGAP2 817.25/896.85 1170.9/832.52 857.05 1001.7 0.22479 30214 24260 0.92887 0.82352 0.17648 0.35296 0.631 True -AATF_m35307525 AATF 405.68/259.29 324.28/166.5 332.49 245.39 -0.43668 11581 8808.4 0.92863 0.17654 0.82346 0.35308 0.631 False -ACTL6A_p179287976 ACTL6A 317.49/985.65 1232.8/599.07 651.57 915.92 0.49065 2.12e+05 81125 0.9281 0.82134 0.17866 0.35732 0.63178 True -ACVRL1_p52306883 ACVRL1 1552.2/1129.5 1162.1/1157 1340.8 1159.5 -0.20944 44671 38239 0.92724 0.1769 0.8231 0.3538 0.63116 False -AFAP1L2_p116164194 AFAP1L2 590.89/365.85 606.29/552.15 478.37 579.22 0.27547 13394 11881 0.92528 0.82259 0.17741 0.35482 0.63146 True -ABTB1_p127395252 ABTB1 526.21/399.59 411.34/771.3 462.9 591.32 0.35256 36401 19284 0.92477 0.82238 0.17762 0.35524 0.63146 True -ACHE_m100491729 ACHE 486.53/831.14 860.53/730.1 658.84 795.32 0.27124 33943 21903 0.92221 0.82179 0.17821 0.35642 0.63178 True -AGBL5_m27275950 AGBL5 354.24/280.6 204.41/275.79 317.42 240.1 -0.4013 2629.6 7049 0.92117 0.17848 0.82152 0.35696 0.63178 False -ACTN4_m39138399 ACTN4 793.73/710.38 763.38/989.87 752.05 876.63 0.22085 14562 18402 0.91829 0.82077 0.17923 0.35847 0.6327 True -ACVR1C_m158401016 ACVR1C 448.31/458.19 456.13/637.41 453.25 546.77 0.27008 8239.7 10476 0.91368 0.81956 0.18044 0.36089 0.63576 True -ABL1_m133729456 ABL1 1145/985.65 1194.3/478.92 1065.3 836.6 -0.34834 1.3428e+05 62837 0.91256 0.18074 0.81926 0.36147 0.63576 False -ADRA1A_p26722419 ADRA1A 567.37/507.92 500.3/370.2 537.65 435.25 -0.30419 5114.7 12668 0.90978 0.18147 0.81853 0.36294 0.6361 False -AHNAK2_p105423957 AHNAK2 676.14/552.32 313.55/654 614.23 483.78 -0.3438 32810 20731 0.90607 0.18245 0.81755 0.3649 0.63841 False -ACVR1_p158655958 ACVR1 410.09/360.52 675.05/325.57 385.31 500.31 0.37596 31149 16213 0.90322 0.81657 0.18343 0.36687 0.64073 True -ABLIM2_m8108279 ABLIM2 673.2/600.27 476.32/574.47 636.74 525.4 -0.27681 3738 15291 0.90039 0.18396 0.81604 0.36791 0.64111 False -ADRM1_p60878704 ADRM1 273.4/671.31 363.39/323.28 472.35 343.34 -0.45909 39986 20641 0.89954 0.18418 0.81582 0.36836 0.64111 False -ACBD6_p180471263 ACBD6 355.71/285.93 278.22/212.28 320.82 245.25 -0.38612 2304.5 7133 0.89497 0.1854 0.8146 0.37081 0.64423 False -AFF3_m100623914 AFF3 242.53/216.67 112.3/222.01 229.6 167.15 -0.45561 3176.2 4916.9 0.89214 0.18616 0.81384 0.37232 0.64573 False -ACBD6_p180471286 ACBD6 723.18/1007 934.98/428.56 865.07 681.77 -0.34308 84248 42419 0.89002 0.18673 0.81327 0.37346 0.64631 False -ADRBK1_p67034172 ADRBK1 1321.4/1795.5 1478.8/1237.1 1558.4 1357.9 -0.19856 70796 51201 0.88616 0.18777 0.81223 0.37553 0.64631 False -ADCK1_p78285311 ADCK1 868.69/701.5 720.48/603.65 785.1 662.06 -0.24556 10401 19304 0.88551 0.18794 0.81206 0.37588 0.64631 False -ACP1_m264983 ACP1 2104.9/1738.7 1569.7/1866.5 1921.8 1718.1 -0.16156 55549 53370 0.88174 0.18896 0.81104 0.37792 0.64866 False -ACBD6_p180471251 ACBD6 488/204.23 565.28/348.46 346.12 456.87 0.39952 31883 15802 0.88105 0.81029 0.18971 0.37941 0.64903 True -ACD_p67694333 ACD 307.2/243.3 157.09/256.91 275.25 207 -0.40941 3511.6 6015.7 0.88058 0.18927 0.81073 0.37855 0.64866 False -ACP1_p272042 ACP1 862.81/1184.6 890.82/876.01 1023.7 883.41 -0.21239 25934 25936 0.87101 0.19187 0.80813 0.38375 0.65417 False -ACAT2_p160183133 ACAT2 520.33/671.31 659.91/739.26 595.82 699.58 0.23126 7272.3 14202 0.8707 0.80804 0.19196 0.38392 0.65417 True -ADNP2_p77891013 ADNP2 742.28/607.37 525.53/601.93 674.83 563.73 -0.25909 6009.6 16312 0.86985 0.19219 0.80781 0.38438 0.65417 False -ACTN4_p39138465 ACTN4 1705.1/1083.3 1088.9/1277.7 1394.2 1183.3 -0.23643 1.0554e+05 59565 0.86409 0.19377 0.80623 0.38754 0.65842 False -ACTN4_p39138381 ACTN4 657.03/635.79 678.21/829.66 646.41 753.93 0.22167 5847.6 15550 0.86227 0.80573 0.19427 0.38854 0.659 True -ADCK2_p140373137 ADCK2 194.02/53.278 121.76/25.176 123.65 73.469 -0.74318 7284.5 4075.4 0.86081 0.19467 0.80533 0.38934 0.65924 False -A1CF_p52603829 A1CF 111.71/252.18 209.46/269.5 181.95 239.48 0.39447 5834.5 4475.7 0.85992 0.80444 0.19556 0.39111 0.661 True -ADRB2_p148206474 ADRB2 148.46/442.21 418.91/358.19 295.33 388.55 0.39458 22495 11835 0.85682 0.80358 0.19642 0.39285 0.66181 True -ADD3_m111860450 ADD3 1597.7/1502.5 1651/1794.9 1550.1 1723 0.15246 7446.8 41157 0.85219 0.80294 0.19706 0.39411 0.66282 True -ABCF1_m30539291 ABCF1 216.07/289.48 192.42/498.37 252.78 345.4 0.44887 24748 11897 0.84914 0.80005 0.19995 0.3999 0.66917 True -ABCF1_p30539272 ABCF1 1111.2/1038.9 948.23/922.36 1075.1 935.29 -0.20075 1473.9 27389 0.84464 0.19916 0.80084 0.39831 0.66838 False -AATK_m79102320 AATK 1474.3/1163.2 1218.9/1844.1 1318.8 1531.5 0.21562 1.2192e+05 63562 0.84385 0.80062 0.19938 0.39875 0.66838 True -ADD1_m2877771 ADD1 530.62/809.83 815.74/747.27 670.23 781.5 0.2213 20661 17680 0.83689 0.79867 0.20133 0.40265 0.67266 True -AGL_m100327112 AGL 1142.1/957.24 1162.1/1210.2 1049.7 1186.1 0.17618 9120.3 26669 0.83566 0.79833 0.20167 0.40335 0.67269 True -ACAD11_p132378489 ACAD11 749.63/1323.1 848.55/870.86 1036.4 859.7 -0.26933 82335 44974 0.833 0.20242 0.79758 0.40484 0.67407 False -ADH7_m100350735 ADH7 812.84/664.2 774.73/453.74 738.52 614.24 -0.26545 31282 22450 0.82949 0.20341 0.79659 0.40683 0.67624 False -AAAS_m53715176 AAAS 801.08/158.06 247.31/379.93 479.57 313.62 -0.61114 1.0777e+05 43359 0.82646 0.20427 0.79573 0.40854 0.67796 False -AHNAK_p62303551 AHNAK 51.445/40.847 117.35/31.47 46.146 74.408 0.67757 1871.7 1174.8 0.82454 0.77515 0.22485 0.4497 0.70415 True -ADRBK1_m67034219 ADRBK1 1109.8/1138.4 970.31/999.03 1124.1 984.67 -0.19084 411.13 28781 0.82169 0.20563 0.79437 0.41126 0.68051 False -ADRB2_p148206467 ADRB2 761.39/925.27 649.82/799.34 843.33 724.58 -0.21868 12303 20904 0.82136 0.20572 0.79428 0.41144 0.68051 False -ADNP2_p77890985 ADNP2 776.09/1280.5 1125.5/1259.4 1028.3 1192.4 0.2135 68076 40069 0.82012 0.79393 0.20607 0.41215 0.68056 True -ACAD9_m128598614 ACAD9 676.14/966.12 1724.2/528.12 821.13 1126.2 0.45527 3.7868e+05 1.3976e+05 0.81598 0.78979 0.21021 0.42041 0.68195 True -ADRA1A_m26722434 ADRA1A 1052.4/1303.5 984.82/1087.1 1178 1036 -0.18516 18383 30322 0.8155 0.20739 0.79261 0.41478 0.68056 False -ADNP_p49510949 ADNP 429.2/889.75 567.8/1132.9 659.48 850.36 0.36626 1.3287e+05 54888 0.81476 0.79189 0.20811 0.41623 0.68056 True -ADH7_p100350692 ADH7 585.01/452.87 409.45/913.2 518.94 661.32 0.3492 67807 30721 0.81236 0.79139 0.20861 0.41723 0.68056 True -AFF4_p132272747 AFF4 527.68/113.66 72.552/331.29 320.67 201.92 -0.66466 59590 24616 0.81217 0.20835 0.79165 0.4167 0.68056 False -ADAM12_p128076650 ADAM12 779.03/648.22 424.59/759.86 713.63 592.22 -0.26861 32379 22366 0.81178 0.20846 0.79154 0.41692 0.68056 False -ADH5_p100002584 ADH5 2173.9/1182.8 1374.1/1439 1678.4 1406.6 -0.25472 2.4665e+05 1.1219e+05 0.81146 0.20855 0.79145 0.4171 0.68056 False -ACTR1A_m104248862 ACTR1A 1056.8/2156 1396.2/1228.5 1606.4 1312.3 -0.29153 3.0907e+05 1.3157e+05 0.81082 0.20874 0.79126 0.41747 0.68056 False -ADRB3_p37823958 ADRB3 2221/976.77 1393/1151.8 1598.9 1272.4 -0.32927 4.0156e+05 1.6225e+05 0.81059 0.2088 0.7912 0.4176 0.68056 False -AHNAK_p62303510 AHNAK 473.3/451.09 598.08/493.22 462.19 545.65 0.239 2872.4 10707 0.80657 0.79004 0.20996 0.41992 0.68195 True -ACIN1_p23538730 ACIN1 946.6/470.63 663.07/457.75 708.61 560.41 -0.33799 67176 33874 0.80541 0.21029 0.78971 0.42058 0.68195 False -ADAD1_p123301314 ADAD1 1456.6/1408.3 1311/1241.6 1432.5 1276.3 -0.16642 1786.1 37697 0.80437 0.21059 0.78941 0.42118 0.68195 False -AGBL5_p27275876 AGBL5 440.96/630.46 434.68/457.17 535.71 445.93 -0.2641 9104 12617 0.79931 0.21206 0.78794 0.42411 0.68531 False -ACSS2_m33501222 ACSS2 662.91/777.87 864.95/787.32 720.39 826.14 0.19735 4810.1 17542 0.79841 0.78768 0.21232 0.42463 0.68531 True -AATF_m35306444 AATF 659.97/809.83 880.72/802.2 734.9 841.46 0.19509 7156 17936 0.79565 0.78688 0.21312 0.42623 0.68679 True -ADRBK2_m26000349 ADRBK2 263.11/385.38 578.53/260.34 324.24 419.43 0.37036 29048 14494 0.79066 0.78467 0.21533 0.43066 0.69033 True -ADCK4_m41220249 ADCK4 1481.6/1994.4 2044.1/1811 1738 1927.5 0.14923 79318 57604 0.78959 0.78512 0.21488 0.42977 0.69033 True -AGTPBP1_m88307605 AGTPBP1 727.59/445.76 502.19/471.48 586.67 486.83 -0.26863 20092 16004 0.78923 0.21499 0.78501 0.42998 0.69033 False -ACBD6_m180471277 ACBD6 1175.9/518.58 833.41/473.19 847.24 653.3 -0.37452 1.4046e+05 60826 0.78714 0.2156 0.7844 0.4312 0.69033 False -AATF_m35307516 AATF 518.86/632.24 627.74/308.98 575.55 468.36 -0.29676 28615 18649 0.78499 0.21623 0.78377 0.43246 0.69124 False -AFTPH_m64778646 AFTPH 999.51/939.48 826.47/1464.2 969.49 1145.3 0.24024 1.0258e+05 50469 0.78274 0.78311 0.21689 0.43378 0.69225 True -ABLIM2_m8108265 ABLIM2 1587.5/1193.4 1145.7/1324 1390.4 1234.9 -0.17107 46764 39900 0.77891 0.21802 0.78198 0.43603 0.69473 False -ADRB1_m115803930 ADRB1 440.96/220.22 451.72/361.62 330.59 406.67 0.298 14211 9653.8 0.77431 0.78054 0.21946 0.43892 0.69735 True -AFAP1L2_m116100394 AFAP1L2 665.85/989.2 859.9/518.4 827.53 689.15 -0.26364 55296 32078 0.77262 0.21987 0.78013 0.43975 0.69735 False -AGPAT3_m45379606 AGPAT3 376.29/294.81 332.48/204.84 335.55 268.66 -0.31967 5732.6 7498.1 0.77258 0.21988 0.78012 0.43977 0.69735 False -ACVR1_p158637019 ACVR1 668.79/1074.4 722.37/761.57 871.62 741.97 -0.23205 41524 28298 0.7707 0.22044 0.77956 0.44088 0.69801 False -ADD1_m2877698 ADD1 477.71/403.14 471.27/246.04 440.42 358.66 -0.29555 14073 11456 0.76401 0.22243 0.77757 0.44486 0.70037 False -AAAS_p53714441 AAAS 401.27/580.73 689.56/474.34 491 581.95 0.2447 19632 14178 0.76378 0.7775 0.2225 0.445 0.70037 True -ACTN1_p69445745 ACTN1 868.69/744.12 765.27/632.26 806.41 698.76 -0.20643 8302.4 19888 0.76329 0.22265 0.77735 0.44529 0.70037 False -ADAM10_p59009761 ADAM10 361.59/237.98 184.85/290.67 299.78 237.76 -0.33316 6619.3 6616.3 0.76281 0.22279 0.77721 0.44558 0.70037 False -ADD3_m111860445 ADD3 1267/694.4 1038.4/534.42 980.71 786.43 -0.31815 1.4549e+05 64981 0.7623 0.22294 0.77706 0.44588 0.70037 False -ACVR2B_p38495827 ACVR2B 1159.7/1097.5 1008.2/1576.9 1128.6 1292.5 0.19548 81842 46555 0.75969 0.77628 0.22372 0.44744 0.70171 True -ADRBK2_m25961092 ADRBK2 1675.7/966.12 1316/1752 1320.9 1534 0.21567 1.7338e+05 80755 0.75004 0.77339 0.22661 0.45323 0.70857 True -AFF4_m132272813 AFF4 953.95/749.45 755.81/1234.2 851.7 995 0.22411 67669 36646 0.74858 0.77294 0.22706 0.45411 0.70884 True -AGL_p100327246 AGL 463.01/253.96 201.88/358.19 358.48 280.04 -0.35518 17033 11058 0.74686 0.22757 0.77243 0.45515 0.70935 False -AFF3_p100623867 AFF3 936.31/561.2 664.33/601.36 748.75 632.84 -0.24229 36168 24264 0.74411 0.22841 0.77159 0.45681 0.71083 False -ADD3_m111860456 ADD3 1484.6/879.09 1263/687.19 1181.8 975.12 -0.27712 1.7455e+05 78472 0.73797 0.23027 0.76973 0.46054 0.71551 False -ADCK2_p140373194 ADCK2 1255.3/1015.8 1528/1049.4 1135.6 1288.7 0.18236 71604 43274 0.73618 0.76919 0.23081 0.46162 0.71609 True -ACSL6_m131329874 ACSL6 795.2/1069.1 1364.6/817.07 932.16 1090.8 0.22657 93708 46815 0.7334 0.76834 0.23166 0.46332 0.7176 True -AFAP1L2_p116100376 AFAP1L2 220.48/513.25 374.12/537.85 366.86 455.98 0.31297 28130 14897 0.73016 0.76705 0.23295 0.46591 0.71859 True -ACTR8_m53916091 ACTR8 351.3/781.42 227.75/624.25 566.36 426 -0.41002 85553 37466 0.72943 0.23287 0.76713 0.46574 0.71859 False -ACAT2_m160183168 ACAT2 658.5/261.06 358.35/354.18 459.78 356.26 -0.3671 39494 20261 0.72882 0.23305 0.76695 0.46611 0.71859 False -AEBP1_m44144355 AEBP1 524.74/1969.5 1010.7/842.82 1247.1 926.75 -0.42796 5.2889e+05 1.9784e+05 0.7266 0.23374 0.76626 0.46747 0.71902 False -ADRB1_m115803938 ADRB1 864.28/1175.7 965.26/1358.4 1020 1161.8 0.18766 62873 38178 0.72587 0.76604 0.23396 0.46792 0.71902 True -ACO2_p41903813 ACO2 1045.1/488.39 1190.5/694.06 766.73 942.27 0.29707 1.3909e+05 58898 0.72332 0.76507 0.23493 0.46986 0.71902 True -ACVR1B_m52369187 ACVR1B 1039.2/1157.9 1012.6/942.38 1098.6 977.48 -0.16831 4755.4 28055 0.72287 0.23488 0.76512 0.46976 0.71902 False -ADRB2_p148206462 ADRB2 1352.3/939.48 1104.7/1499.7 1145.9 1302.2 0.18433 81608 46805 0.7225 0.76501 0.23499 0.46999 0.71902 True -AFAP1L2_m116092987 AFAP1L2 878.98/465.3 635.94/975.57 672.14 805.75 0.26122 71621 34700 0.71726 0.76336 0.23664 0.47328 0.72295 True -ACTL7A_m111624669 ACTL7A 554.14/381.83 603.13/481.78 467.99 542.45 0.21262 11105 10939 0.71201 0.76177 0.23823 0.47646 0.72669 True -AFF1_p87967926 AFF1 792.26/1030 1313.5/799.34 911.16 1056.4 0.2132 80230 41932 0.70942 0.76097 0.23903 0.47807 0.72803 True -ADAM10_m59009866 ADAM10 610/626.91 339.42/695.77 618.45 517.6 -0.25638 31819 20475 0.70487 0.24045 0.75955 0.48089 0.73 False -ADARB1_p46595705 ADARB1 1987.3/1898.5 1793.6/2477.5 1942.9 2135.6 0.13637 1.1891e+05 74917 0.70405 0.7593 0.2407 0.4814 0.73 True -ACTN4_p39138403 ACTN4 837.83/1486.5 1100.3/1614.7 1162.1 1357.5 0.22396 1.7134e+05 77026 0.70382 0.75923 0.24077 0.48155 0.73 True -ACO2_p41865164 ACO2 646.74/310.79 666.22/487.5 478.77 576.86 0.26838 36201 19490 0.70263 0.75878 0.24122 0.48243 0.73023 True -AEBP1_p44144293 AEBP1 901.03/1152.6 991.13/1306.9 1026.8 1149 0.16205 40743 30930 0.69475 0.75639 0.24361 0.48721 0.73496 True -ADCK2_m140373177 ADCK2 582.07/804.5 745.08/427.42 693.29 586.25 -0.24156 37597 23739 0.69472 0.24362 0.75638 0.48723 0.73496 False -ACAT2_p160183141 ACAT2 1243.5/843.58 613.86/1148.4 1043.5 881.11 -0.24384 1.1141e+05 54802 0.69387 0.24388 0.75612 0.48776 0.73496 False -ACTR5_m37377155 ACTR5 95.542/369.4 415.76/207.7 232.47 311.73 0.42168 29571 13180 0.69038 0.74965 0.25035 0.50069 0.74006 True -AFMID_p76183465 AFMID 158.75/598.49 317.97/236.88 378.62 277.43 -0.44726 49988 22380 0.69003 0.24509 0.75491 0.49018 0.73555 False -ABI1_m27149743 ABI1 1412.5/703.28 808.8/964.13 1057.9 886.46 -0.25482 1.318e+05 61867 0.68931 0.24531 0.75469 0.49063 0.73555 False -ACTR1A_p104262374 ACTR1A 1337.6/722.81 750.13/991.02 1030.2 870.57 -0.24263 1.0899e+05 53744 0.68856 0.24555 0.75445 0.4911 0.73555 False -ACAD11_m132378578 ACAD11 526.21/593.17 497.77/463.47 559.69 480.62 -0.21931 1414.9 13247 0.687 0.24604 0.75396 0.49208 0.73555 False -ABHD14B_m52004113 ABHD14B 579.13/651.77 926.78/517.82 615.45 722.3 0.23061 43130 24192 0.68696 0.75394 0.24606 0.49212 0.73555 True -ABCB8_p150725675 ABCB8 943.66/756.55 615.12/1467.6 850.11 1041.4 0.29247 1.9045e+05 77545 0.68688 0.75364 0.24636 0.49271 0.73555 True -ADD1_m2877722 ADD1 479.18/362.29 508.5/467.47 420.74 487.98 0.21345 3836.3 9643.7 0.6848 0.75326 0.24674 0.49347 0.73555 True -AGFG1_m228337217 AGFG1 336.6/618.03 361.5/429.14 477.32 395.32 -0.2713 20944 14379 0.68388 0.24702 0.75298 0.49405 0.73555 False -ABL2_p179100556 ABL2 605.59/502.59 842.87/465.18 554.09 654.03 0.23883 38313 21504 0.68149 0.7522 0.2478 0.4956 0.73676 True -ABCF1_p30545605 ABCF1 501.23/387.16 436.58/314.7 444.19 375.64 -0.24125 6966.4 10244 0.67735 0.24909 0.75091 0.49819 0.73951 False -ADCK1_p78285341 ADCK1 1168.5/907.51 879.46/977.86 1038 928.66 -0.16046 19456 26340 0.67388 0.25019 0.74981 0.50039 0.74006 False -ACSS2_m33500940 ACSS2 1655.1/943.03 1069.4/2038.1 1299.1 1553.7 0.2581 3.6137e+05 1.43e+05 0.6735 0.74961 0.25039 0.50078 0.74006 True -ACO2_p41895844 ACO2 264.58/525.68 376.01/576.76 395.13 476.38 0.26918 27119 15035 0.66268 0.74607 0.25393 0.50786 0.74941 True -AFF2_m147924509 AFF2 122/81.694 31.544/115.01 101.85 73.276 -0.46951 2147.7 2043.7 0.65991 0.25466 0.74534 0.50931 0.75045 False -AFAP1L2_m116100466 AFAP1L2 457.13/433.33 488.94/534.42 445.23 511.68 0.20027 658.65 10270 0.65568 0.74398 0.25602 0.51203 0.75335 True -ACP1_p272099 ACP1 352.77/325 345.73/444.01 338.88 394.87 0.21999 2607.8 7581.1 0.64301 0.73988 0.26012 0.52024 0.7643 True -AHCTF1_m247068844 AHCTF1 1106.8/1056.7 1743.1/807.35 1081.8 1275.2 0.23721 2.1956e+05 91572 0.63943 0.73868 0.26132 0.52263 0.76668 True -ADCY1_m45614206 ADCY1 179.32/312.57 409.45/200.84 245.95 305.14 0.31001 15318 8644.5 0.63668 0.73676 0.26324 0.52648 0.7694 True -ACTR1A_p104248833 ACTR1A 804.02/745.9 651.08/1125.5 774.96 888.28 0.19666 57109 31721 0.63626 0.7377 0.2623 0.52461 0.76845 True -A1CF_m52603842 A1CF 402.74/431.56 198.1/489.22 417.15 343.66 -0.27886 21395 13500 0.6329 0.2634 0.7366 0.5268 0.7694 False -ACVR1C_m158401097 ACVR1C 961.3/788.52 982.3/952.68 874.91 967.49 0.14496 7682 21777 0.62737 0.73479 0.26521 0.53042 0.77356 True -ACVR1C_p158443795 ACVR1C 1481.6/1044.3 1328.7/1444.2 1262.9 1386.4 0.13447 51161 38897 0.62607 0.73437 0.26563 0.53127 0.77367 True -ACTL6A_p179287920 ACTL6A 1202.4/1166.8 1205/946.39 1184.6 1075.7 -0.13898 17036 30511 0.62335 0.26653 0.73347 0.53305 0.77462 False -AFF4_m132272866 AFF4 1199.4/1466.9 1408.1/1490.5 1333.2 1449.3 0.12044 19588 34799 0.62272 0.73326 0.26674 0.53347 0.77462 True -ABI1_p27149733 ABI1 123.47/470.63 249.2/199.12 297.05 224.16 -0.40459 30757 14617 0.61835 0.26817 0.73183 0.53634 0.77589 False -AGPAT3_p45379613 AGPAT3 598.24/305.46 466.86/275.22 451.85 371.04 -0.28358 30611 17164 0.61746 0.26847 0.73153 0.53693 0.77589 False -ADAD1_m123301308 ADAD1 780.5/754.78 553.92/1272.5 767.64 913.23 0.25024 1.2927e+05 55640 0.6172 0.7313 0.2687 0.53741 0.77589 True -ACSS2_p33470688 ACSS2 880.45/621.58 671.9/1040.8 751.02 856.35 0.18911 50775 29175 0.61667 0.73127 0.26873 0.53746 0.77589 True -ADAMTS5_m28338537 ADAMTS5 981.87/1282.2 680.73/1288.6 1132.1 984.64 -0.20108 1.1492e+05 57645 0.61399 0.26961 0.73039 0.53923 0.77732 False -ABL2_p179095768 ABL2 535.03/577.18 807.54/470.33 556.11 638.94 0.19997 28871 18392 0.61074 0.72931 0.27069 0.54138 0.77843 True -ADAM12_p128076618 ADAM12 120.53/232.65 192.42/234.59 176.59 213.51 0.27249 3587.3 3672.2 0.60923 0.72833 0.27167 0.54334 0.77843 True -AES_p3061186 AES 238.12/266.39 169.71/461.18 252.26 315.44 0.32135 21438 10786 0.60843 0.72648 0.27352 0.54704 0.77843 True -AFTPH_p64778754 AFTPH 849.59/861.33 762.11/771.87 855.46 766.99 -0.15729 58.314 21239 0.60703 0.27191 0.72809 0.54383 0.77843 False -ABL1_p133729469 ABL1 930.43/1090.4 1011.3/1203.3 1010.4 1107.3 0.13196 15614 25562 0.60593 0.72772 0.27228 0.54456 0.77843 True -ABI1_p27112180 ABI1 455.66/380.05 335.63/381.64 417.86 358.64 -0.21991 1958.4 9570.3 0.60534 0.27248 0.72752 0.54495 0.77843 False -ACTR3_p114684946 ACTR3 770.21/548.77 752.65/367.91 659.49 560.28 -0.23481 49265 27022 0.60358 0.27306 0.72694 0.54612 0.77843 False -ADD1_p2877743 ADD1 573.25/1397.7 728.68/910.34 985.46 819.51 -0.26574 1.7817e+05 75963 0.6025 0.27342 0.72658 0.54684 0.77843 False -ACRC_p70814187 ACRC 961.3/1097.5 959.58/905.19 1029.4 932.39 -0.14268 5380 26097 0.60062 0.27405 0.72595 0.54809 0.77843 False -ACIN1_p23538719 ACIN1 640.86/559.42 711.64/632.26 600.14 671.95 0.16279 3233.6 14317 0.60014 0.72579 0.27421 0.54841 0.77843 True -AFF2_m147967470 AFF2 892.21/1278.7 893.97/1599.8 1085.4 1246.9 0.19988 1.619e+05 72420 0.59993 0.72572 0.27428 0.54857 0.77843 True -AGTPBP1_p88307705 AGTPBP1 655.56/449.31 451.72/516.68 552.44 484.2 -0.18985 11690 13056 0.59722 0.27518 0.72482 0.55036 0.7796 False -AGFG1_p228337142 AGFG1 0/42.623 0/78.389 21.311 39.194 0.84922 1990.4 897.03 0.59709 0.63864 0.36136 0.72272 0.86416 True -AFTPH_p64778649 AFTPH 1724.2/1500.7 1326.8/1650.7 1612.4 1488.8 -0.11505 38727 43003 0.59634 0.27547 0.72453 0.55095 0.7796 False -AFF1_m87967374 AFF1 257.23/191.8 170.34/379.93 224.51 275.13 0.29215 12052 7214.6 0.59595 0.72326 0.27674 0.55348 0.78097 True -AFF2_m147891407 AFF2 355.71/374.72 271.91/602.51 365.22 437.21 0.25892 27413 14631 0.5952 0.72379 0.27621 0.55241 0.78056 True -ADRB3_p37823913 ADRB3 454.19/381.83 525.53/425.7 418.01 475.62 0.18585 3800.5 9574.2 0.58875 0.72198 0.27802 0.55604 0.78336 True -AFMID_p76183445 AFMID 911.32/856.01 938.13/1003.6 883.66 970.87 0.13563 1836.6 22020 0.58768 0.72163 0.27837 0.55674 0.78336 True -ABHD14B_p52004019 ABHD14B 41.156/284.15 169.08/259.77 162.65 214.42 0.39653 16818 7840.3 0.58468 0.71105 0.28895 0.5779 0.79519 True -AGPAT5_m6566212 AGPAT5 2451.7/2452.6 2151.3/2447.8 2452.2 2299.6 -0.092657 21972 68578 0.58273 0.28004 0.71996 0.56008 0.78694 False -ACLY_p40070039 ACLY 314.55/252.18 88.955/359.9 283.37 224.43 -0.33509 19325 10584 0.57916 0.28124 0.71876 0.56248 0.78852 False -ABHD14B_m52004073 ABHD14B 473.3/339.21 388.63/312.41 406.25 350.52 -0.21232 5947.5 9275.2 0.57872 0.28139 0.71861 0.56278 0.78852 False -AFF1_m87967978 AFF1 549.73/365.85 375.38/697.49 457.79 536.43 0.22826 34392 18526 0.57781 0.71819 0.28181 0.56361 0.78858 True -ACSS2_m33470722 ACSS2 734.94/358.74 541.93/378.78 546.84 460.36 -0.24786 42035 22618 0.57532 0.28254 0.71746 0.56508 0.78884 False -ADAR_p154574157 ADAR 1030.4/522.13 676.31/663.73 776.25 670.02 -0.21203 64619 34248 0.57406 0.28296 0.71704 0.56593 0.78884 False -ABL2_p179100578 ABL2 623.23/570.08 549.5/506.95 596.65 528.23 -0.17541 1158.8 14224 0.57371 0.28308 0.71692 0.56616 0.78884 False -ABHD14B_p52004076 ABHD14B 2079.9/1886.1 2073.1/1622.7 1983 1847.9 -0.10171 60105 56127 0.57007 0.28431 0.71569 0.56863 0.79112 False -ADAMTS5_m28338702 ADAMTS5 602.65/497.27 627.1/602.51 549.96 614.81 0.16054 2927.6 12991 0.56896 0.71531 0.28469 0.56938 0.79112 True -AGAP2_p58128423 AGAP2 421.85/511.47 400.62/414.83 466.66 407.72 -0.19434 2058.5 10822 0.56659 0.2855 0.7145 0.571 0.7914 False -ADI1_m3523239 ADI1 579.13/316.12 406.29/636.84 447.62 521.57 0.22011 30581 17082 0.56575 0.71413 0.28587 0.57174 0.7914 True -ACTR5_m37377161 ACTR5 342.48/548.77 531.21/203.12 445.62 367.17 -0.2787 37549 19370 0.56516 0.28598 0.71402 0.57196 0.7914 False -ADRBK2_m26000370 ADRBK2 711.42/431.56 571.59/423.41 571.49 497.5 -0.19965 25069 17395 0.56098 0.28741 0.71259 0.57481 0.79333 False -ADAM10_p58974481 ADAM10 1339.1/1152.6 1468.1/1225 1245.8 1346.6 0.1121 23459 32271 0.56078 0.71253 0.28747 0.57495 0.79333 True -ACTL6B_p100253094 ACTL6B 945.13/1408.3 1386.1/645.99 1176.7 1016 -0.21164 1.9057e+05 83712 0.55546 0.28929 0.71071 0.57858 0.79519 False -AHCY_m32883267 AHCY 789.32/671.31 723.63/588.77 730.31 656.2 -0.15415 8028.3 17811 0.55531 0.28934 0.71066 0.57868 0.79519 False -AGTPBP1_p88307675 AGTPBP1 1175.9/1147.3 909.11/1223.3 1161.6 1066.2 -0.12347 24887 29852 0.55193 0.2905 0.7095 0.58099 0.79727 False -ADCK3_p227149079 ADCK3 2312.1/1088.7 1301.5/1652.5 1700.4 1477 -0.20307 4.05e+05 1.6541e+05 0.54929 0.2914 0.7086 0.58281 0.79866 False -AEBP1_m44144289 AEBP1 1960.8/2106.3 1629/2164 2033.5 1896.5 -0.10062 76855 62737 0.54723 0.29211 0.70789 0.58422 0.79877 False -ADRBK2_p26040592 ADRBK2 426.26/607.37 258.66/615.09 516.82 436.88 -0.24191 39961 21402 0.54684 0.29224 0.70776 0.58449 0.79877 False -ACTN4_m39138527 ACTN4 214.6/159.84 251.09/38.336 187.22 144.72 -0.36926 12066 6634.6 0.54379 0.29329 0.70671 0.58658 0.80054 False -AHRR_m353878 AHRR 1.4699/83.47 0/62.94 42.47 31.47 -0.4209 2671.3 1392.8 0.54162 0.29404 0.70596 0.58808 0.80117 False -ABCC1_m16101777 ABCC1 699.66/728.14 938.13/639.13 713.9 788.63 0.14344 22554 19096 0.5408 0.70568 0.29432 0.58865 0.80117 True -AHNAK_p62303477 AHNAK 655.56/500.82 554.55/475.48 578.19 515.02 -0.16662 7549.5 13735 0.53903 0.29493 0.70507 0.58987 0.80117 False -A1CF_p52601638 A1CF 1093.6/815.16 777.89/1377.2 954.37 1077.6 0.17498 1.0919e+05 52388 0.53823 0.70479 0.29521 0.59043 0.80117 True -ACTRT3_p169487280 ACTRT3 1469.9/1001.6 1264.9/975 1235.8 1120 -0.14182 75827 46596 0.53639 0.29584 0.70416 0.59169 0.80117 False -ACVR2A_p148653997 ACVR2A 1603.6/1811.5 1557.7/1627.9 1707.5 1592.8 -0.10034 12031 45836 0.53615 0.29593 0.70407 0.59186 0.80117 False -AFF3_p100623873 AFF3 318.96/438.66 435.31/210.56 378.81 322.94 -0.22956 16210 11124 0.53008 0.29803 0.70197 0.59606 0.80226 False -ACVRL1_p52306287 ACVRL1 1687.4/1525.5 1391.1/2109.1 1606.5 1750.1 0.12345 1.3541e+05 73689 0.52904 0.70161 0.29839 0.59678 0.80226 True -ADIRF_p88728292 ADIRF 1074.5/950.13 1100.9/743.84 1012.3 922.37 -0.13409 35740 28990 0.52821 0.29868 0.70132 0.59736 0.80226 False -ADD3_m111860540 ADD3 1167.1/1316 1367.1/901.19 1241.5 1134.2 -0.13038 59821 41372 0.52785 0.2988 0.7012 0.5976 0.80226 False -ADRA1A_p26722329 ADRA1A 1571.3/1456.3 1434/1382.4 1513.8 1408.2 -0.10423 3973.3 40085 0.52735 0.29897 0.70103 0.59795 0.80226 False -AAAS_p53714405 AAAS 1644.8/1520.2 1395.5/1553.5 1582.5 1474.5 -0.10191 10116 42116 0.52625 0.29936 0.70064 0.59871 0.80226 False -ACRC_p70800697 ACRC 232.24/681.96 275.7/467.47 457.1 371.59 -0.2981 59757 26969 0.52615 0.29939 0.70061 0.59879 0.80226 False -AGFG1_p228337168 AGFG1 667.32/667.76 492.09/709.5 667.54 600.8 -0.15173 11817 16116 0.52571 0.29954 0.70046 0.59909 0.80226 False -ADRBK2_p25960997 ADRBK2 1311.1/429.78 1237.2/823.37 870.45 1030.3 0.24293 2.37e+05 93436 0.52285 0.6988 0.3012 0.60241 0.80403 True -AEN_m89169507 AEN 567.37/737.02 333.11/790.18 652.19 561.65 -0.21528 59424 30278 0.52055 0.30134 0.69866 0.60268 0.80403 False -ADARB2_m1779246 ADARB2 1863.8/1585.9 1761.4/1464.2 1724.9 1612.8 -0.096827 41390 46354 0.52035 0.30141 0.69859 0.60282 0.80403 False -AGAP2_p58129143 AGAP2 756.98/500.82 792.4/260.91 628.9 526.66 -0.25553 87024 39063 0.51878 0.30196 0.69804 0.60391 0.80441 False -ABT1_m26597238 ABT1 488/367.62 312.29/441.15 427.81 376.72 -0.18301 7773.9 9824.3 0.51545 0.30312 0.69688 0.60624 0.80644 False -ADH7_p100349706 ADH7 464.48/394.26 171.6/547.01 429.37 359.3 -0.25636 36465 18731 0.51365 0.30375 0.69625 0.6075 0.80704 False -ADRA1A_p26722412 ADRA1A 1012.7/983.87 1008.8/825.66 998.31 917.23 -0.12208 8592.9 25221 0.51056 0.30483 0.69517 0.60966 0.80883 False -AGAP3_m150784003 AGAP3 1387.6/1120.6 1214.5/1111.2 1254.1 1162.8 -0.10892 20481 32510 0.50621 0.30636 0.69364 0.61271 0.81147 False -ADCK5_p145597774 ADCK5 576.19/541.66 670.64/303.83 558.93 487.23 -0.19767 33935 20130 0.5054 0.30664 0.69336 0.61328 0.81147 False -ACAD11_p132378547 ACAD11 51.445/465.3 251.09/143.05 258.37 197.07 -0.38901 45737 18984 0.50398 0.30714 0.69286 0.61428 0.81172 False -AGL_p100327114 AGL 47.036/147.4 239.74/30.898 97.22 135.32 0.47289 13422 5734.9 0.50309 0.65854 0.34146 0.68293 0.84021 True -ACVR1_m158637054 ACVR1 291.03/866.66 570.32/389.08 578.85 479.7 -0.27053 91049 39518 0.50227 0.30774 0.69226 0.61548 0.81224 False -AFF4_m132272841 AFF4 241.06/637.57 282.64/802.2 439.31 542.42 0.30354 1.0679e+05 42342 0.50107 0.68671 0.31329 0.62659 0.81924 True -ACRC_p70800656 ACRC 258.7/685.52 533.73/560.74 472.11 547.24 0.21263 45726 22550 0.5003 0.69131 0.30869 0.61738 0.8126 True -ADCY1_p45614284 ADCY1 435.08/504.37 371.59/463.47 469.73 417.53 -0.16955 3310.3 10901 0.49993 0.30856 0.69144 0.61713 0.8126 False -ADD3_p111860545 ADD3 1381.7/1209.4 1247.3/1161 1295.5 1204.1 -0.10551 9280.7 33708 0.49803 0.30923 0.69077 0.61846 0.81295 False -AGFG1_m228337208 AGFG1 671.73/781.42 554.55/766.72 726.57 660.64 -0.13705 14262 17710 0.49547 0.31013 0.68987 0.62027 0.81426 False -ADNP_m49520451 ADNP 1649.2/783.19 773.47/1332 1216.2 1052.8 -0.20802 2.6549e+05 1.0944e+05 0.49427 0.31056 0.68944 0.62111 0.81429 False -ADRM1_p60878682 ADRM1 1011.3/713.93 792.4/1100.3 862.6 946.35 0.13353 45805 29559 0.48712 0.68691 0.31309 0.62617 0.81924 True -ADAP1_m975116 ADAP1 586.48/335.65 379.8/433.71 461.07 406.75 -0.1804 16455 12603 0.48382 0.31426 0.68574 0.62851 0.81924 False -AFTPH_p64778890 AFTPH 620.29/529.23 366.55/657.44 574.76 511.99 -0.16653 23227 16839 0.48371 0.31429 0.68571 0.62859 0.81924 False -AES_m3057717 AES 999.51/1026.5 938.13/1242.2 1013 1090.2 0.10581 23297 25635 0.48195 0.68508 0.31492 0.62984 0.81924 True -ADRB3_p37823900 ADRB3 180.79/239.75 279.48/205.41 210.27 242.45 0.20451 2240.7 4458.9 0.48184 0.68478 0.31522 0.63043 0.81924 True -ADCY1_m45614270 ADCY1 667.32/614.48 543.2/873.15 640.9 708.17 0.14379 27915 19573 0.48085 0.68469 0.31531 0.63063 0.81924 True -ACTRT3_m169487252 ACTRT3 283.69/854.23 348.88/597.93 568.96 473.41 -0.26473 96887 41290 0.47513 0.31735 0.68265 0.6347 0.82346 False -ABCC1_p16101667 ABCC1 320.43/248.63 247.31/397.67 284.53 322.49 0.18006 6940.6 6474.6 0.4717 0.68136 0.31864 0.63727 0.82573 True -ABCC1_p16101721 ABCC1 1480.2/1214.7 882.61/1568.3 1347.5 1225.5 -0.13678 1.3517e+05 68533 0.46592 0.32064 0.67936 0.64127 0.82911 False -AFMID_m76183481 AFMID 701.13/978.55 617.01/908.62 839.84 762.82 -0.1386 40500 27372 0.46554 0.32077 0.67923 0.64155 0.82911 False -ABL2_m179100554 ABL2 1055.4/722.81 915.42/1007.6 889.09 961.52 0.11286 29773 24704 0.4608 0.67753 0.32247 0.64494 0.8294 True -AAK1_m69870049 AAK1 535.03/825.82 437.21/1147.8 680.42 792.5 0.21968 1.4737e+05 60100 0.45717 0.67533 0.32467 0.64934 0.8294 True -ADK_p76074446 ADK 1505.1/451.09 653.6/991.02 978.12 822.31 -0.25005 3.0622e+05 1.1851e+05 0.45682 0.3239 0.6761 0.6478 0.8294 False -ACP1_m264989 ACP1 1456.6/1333.7 1753.9/1236.5 1395.2 1495.2 0.099788 70699 47970 0.45653 0.67599 0.32401 0.64801 0.8294 True -ADNP2_p77875511 ADNP2 1293.5/1303.5 1006.9/1416.1 1298.5 1211.5 -0.099961 41896 36495 0.45537 0.32442 0.67558 0.64885 0.8294 False -ADAD1_m123301345 ADAD1 514.45/468.85 733.09/374.21 491.65 553.65 0.17101 32720 18552 0.45517 0.67546 0.32454 0.64908 0.8294 True -ACTR5_p37377204 ACTR5 291.03/305.46 359.61/310.69 298.25 335.15 0.16776 650.16 6577.1 0.45502 0.67542 0.32458 0.64917 0.8294 True -A1CF_m52595977 A1CF 313.08/486.61 557.08/100.13 399.85 328.6 -0.28232 59727 25984 0.45424 0.32483 0.67517 0.64966 0.8294 False -ACSL6_p131329878 ACSL6 908.38/1435 998.7/1151.8 1171.7 1075.2 -0.12379 75183 45155 0.45376 0.325 0.675 0.65 0.8294 False -ACD_p67694194 ACD 1439/1046 1161.5/1155.8 1242.5 1158.6 -0.10075 38614 34322 0.45277 0.32536 0.67464 0.65071 0.8294 False -ABCC1_p16043597 ABCC1 307.2/412.02 305.35/332.44 359.61 318.89 -0.17285 2930.1 8098.6 0.45251 0.32545 0.67455 0.6509 0.8294 False -ADPRHL2_m36554563 ADPRHL2 1209.7/420.9 786.72/609.95 815.3 698.33 -0.22312 1.6337e+05 67877 0.4506 0.32614 0.67386 0.65228 0.8301 False -ABT1_m26597359 ABT1 358.65/394.26 593.04/268.35 376.45 430.69 0.19371 26672 14572 0.44933 0.67311 0.32689 0.65379 0.83096 True -AEBP2_m19615553 AEBP2 1371.4/534.56 865.58/796.48 952.97 831.03 -0.19732 1.7626e+05 74721 0.44657 0.32759 0.67241 0.65519 0.83141 False -ADRA1B_p159343940 ADRA1B 1074.5/1252 1269.4/1211.3 1163.3 1240.3 0.092473 8724.8 29900 0.4457 0.67209 0.32791 0.65581 0.83141 True -ACVR2B_p38518854 ACVR2B 2146/1630.3 1712.9/1851 1888.2 1781.9 -0.083494 71255 57928 0.44137 0.32947 0.67053 0.65895 0.83325 False -ACD_p67694350 ACD 543.85/245.08 295.89/388.51 394.47 342.2 -0.20451 24461 14138 0.44042 0.32982 0.67018 0.65963 0.83325 False -AGAP3_p150783915 AGAP3 837.83/719.26 374.75/993.88 778.54 684.31 -0.18586 99345 45865 0.44025 0.32988 0.67012 0.65976 0.83325 False -ADAM12_m128019025 ADAM12 560.02/861.33 956.43/613.95 710.68 785.19 0.14365 52020 28860 0.43861 0.66952 0.33048 0.66095 0.8337 True -ACTN1_p69392370 ACTN1 2125.4/1632.1 1962.7/1996.9 1878.8 1979.8 0.075535 61138 54367 0.43334 0.66762 0.33238 0.66477 0.83695 True -ACVR1B_m52369211 ACVR1B 1512.5/1339.1 1062.4/1594.1 1425.8 1328.3 -0.10214 78190 51064 0.43157 0.33303 0.66697 0.66605 0.83695 False -ADCY1_p45614294 ADCY1 149.93/312.57 287.69/239.74 231.25 263.71 0.18878 7187.5 5700 0.43005 0.66606 0.33394 0.66789 0.83695 True -ADRB3_p37823952 ADRB3 1233.2/1458.1 1519.8/1332.6 1345.6 1426.2 0.083838 21399 35162 0.4297 0.66629 0.33371 0.66742 0.83695 True -ACTN1_p69445708 ACTN1 179.32/252.18 249.2/240.32 215.75 244.76 0.18118 1346.9 4588.3 0.42819 0.6655 0.3345 0.669 0.83695 True -ACSL6_p131329824 ACSL6 1027.4/978.55 1338.1/414.83 1003 876.47 -0.19432 2.1371e+05 88139 0.42682 0.33475 0.66525 0.66951 0.83695 False -ACTL6B_p100253037 ACTL6B 1394.9/816.94 642.25/1321.2 1105.9 981.71 -0.17172 1.9875e+05 85092 0.42596 0.33507 0.66493 0.67014 0.83695 False -AAK1_m69870119 AAK1 595.3/488.39 508.5/478.92 541.84 493.71 -0.13396 3076.3 12778 0.42583 0.33512 0.66488 0.67023 0.83695 False -ACBD6_m180471378 ACBD6 592.36/577.18 782.93/494.36 584.77 638.65 0.12695 20876 16231 0.4229 0.66382 0.33618 0.67237 0.83796 True -ABI1_m27149764 ABI1 204.31/332.1 203.15/268.93 268.21 236.04 -0.18361 5164.3 5844.6 0.42122 0.3368 0.6632 0.67359 0.83796 False -ADRA1B_m159343934 ADRA1B 748.16/1200.5 1029.6/751.27 974.35 890.44 -0.12978 70529 39875 0.4202 0.33717 0.66283 0.67434 0.83796 False -AEBP2_p19615440 AEBP2 1030.4/1092.2 878.2/1106.6 1061.3 992.4 -0.096738 13997 26998 0.4193 0.3375 0.6625 0.675 0.83796 False -AHRR_m353898 AHRR 561.49/170.49 199.36/411.97 365.99 305.67 -0.25908 49521 22013 0.41892 0.33764 0.66236 0.67528 0.83796 False -ADPRHL2_p36554568 ADPRHL2 492.41/793.85 555.81/863.99 643.13 709.9 0.14231 46460 25795 0.41577 0.6612 0.3388 0.6776 0.83796 True -AFMID_p76187060 AFMID 454.19/1129.5 697.13/699.21 791.85 698.17 -0.1814 1.1401e+05 50997 0.41523 0.33899 0.66101 0.67797 0.83796 False -ADAP1_m975132 ADAP1 956.89/941.25 1210.7/826.8 949.07 1018.7 0.1021 36901 28194 0.41493 0.6609 0.3391 0.6782 0.83796 True -ADAD1_m123301332 ADAD1 1045.1/1118.8 1071.9/954.4 1082 1013.1 -0.094726 4811 27584 0.41438 0.3393 0.6607 0.67859 0.83796 False -ADNP_p49510837 ADNP 495.35/301.91 336.9/555.02 398.63 445.96 0.16147 21248 13137 0.41291 0.66008 0.33992 0.67985 0.83796 True -ADAM10_p59009767 ADAM10 886.33/751.23 969.05/521.83 818.78 745.44 -0.13521 54564 31673 0.4121 0.34013 0.65987 0.68027 0.83796 False -ACSS2_m33500955 ACSS2 626.17/884.42 517.33/858.27 755.29 687.8 -0.13486 45735 27572 0.40647 0.3422 0.6578 0.6844 0.84098 False -AFAP1L2_p116100433 AFAP1L2 333.66/296.58 346.36/216.28 315.12 281.32 -0.16314 4573.5 6992.3 0.40436 0.34297 0.65703 0.68595 0.84101 False -AHNAK2_m105423801 AHNAK2 1215.6/1655.2 1491.4/1543.7 1435.4 1517.6 0.080286 48996 41520 0.40341 0.65668 0.34332 0.68664 0.84101 True -ACTL6B_m100253076 ACTL6B 924.55/1115.3 1301.5/888.03 1019.9 1094.8 0.10208 51842 34500 0.403 0.65652 0.34348 0.68695 0.84101 True -ADRB2_m148206447 ADRB2 826.07/822.26 880.09/654 824.17 767.05 -0.10349 12783 20376 0.40014 0.34453 0.65547 0.68905 0.84176 False -AGTPBP1_m88307687 AGTPBP1 1988.7/1243.2 1431.5/1579.2 1615.9 1505.4 -0.10221 1.4443e+05 76881 0.39887 0.345 0.655 0.68999 0.84176 False -ADRBK2_m25961108 ADRBK2 1031.8/392.48 904.7/696.34 712.17 800.52 0.1685 1.1305e+05 49230 0.39821 0.65453 0.34547 0.69094 0.84176 True -ADH7_m100350690 ADH7 170.51/715.71 366.55/382.22 443.11 374.38 -0.24254 74373 31601 0.39783 0.34538 0.65462 0.69076 0.84176 False -ADD1_p2877644 ADD1 820.19/1019.4 1227.7/763.29 919.79 995.5 0.114 63843 36630 0.39558 0.65379 0.34621 0.69242 0.84235 True -ACLY_m40070116 ACLY 1009.8/1102.9 1237.2/1004.8 1056.3 1121 0.085596 15670 26858 0.39437 0.65335 0.34665 0.69331 0.84235 True -ADD1_m2877776 ADD1 565.9/438.66 539.41/550.44 502.28 544.92 0.11734 4078 11744 0.39351 0.65303 0.34697 0.69395 0.84235 True -ADAR_m154574108 ADAR 1594.8/1092.2 1489.5/1000.2 1343.5 1244.9 -0.10995 1.2302e+05 64407 0.38874 0.34873 0.65127 0.69747 0.84559 False -ACTR8_m53916103 ACTR8 903.97/1445.6 1482.6/1055.1 1174.8 1268.8 0.11102 1.1903e+05 59831 0.3845 0.6497 0.3503 0.70061 0.84801 True -AATK_m79102325 AATK 930.43/1282.2 1182.3/1161.5 1106.3 1171.9 0.083003 31050 29201 0.38375 0.64942 0.35058 0.70116 0.84801 True -ADPRHL2_p36554580 ADPRHL2 849.59/935.92 934.35/739.83 892.75 837.09 -0.092776 11323 22272 0.373 0.35457 0.64543 0.70915 0.85664 False -ADAM10_m59041689 ADAM10 89.662/99.453 49.84/176.8 94.558 113.32 0.25866 4053.9 2573.9 0.36987 0.63281 0.36719 0.73438 0.86924 True -ACVR1_m158655997 ACVR1 1349.3/2315.8 1698.4/1722.3 1832.6 1710.3 -0.099568 2.3367e+05 1.1095e+05 0.3671 0.35677 0.64323 0.71354 0.8609 False -ADCK3_p227149124 ADCK3 1208.2/1010.5 1032.8/1063.1 1109.4 1047.9 -0.082114 10004 28363 0.36479 0.35764 0.64236 0.71527 0.86195 False -ACP1_m272095 ACP1 511.52/669.53 495.25/783.89 590.52 639.57 0.11492 27071 18398 0.36158 0.64117 0.35883 0.71767 0.8638 True -ADAP1_m975089 ADAP1 471.83/632.24 523.64/662.59 552.03 593.11 0.10337 11259 13046 0.35966 0.64045 0.35955 0.7191 0.86416 True -AEN_p89169516 AEN 1444.9/1069.1 1717.3/994.45 1257 1355.9 0.10914 1.6592e+05 77036 0.3562 0.63915 0.36085 0.72169 0.86416 True -AES_m3057678 AES 868.69/792.07 746.34/1016.2 830.38 881.27 0.085704 19673 20547 0.35499 0.6387 0.3613 0.7226 0.86416 True -ADI1_m3523156 ADI1 768.74/722.81 643.51/948.68 745.78 796.09 0.09407 23809 20091 0.35498 0.6387 0.3613 0.72261 0.86416 True -ACVR1_p158637035 ACVR1 2013.7/1378.1 1688.3/1887.1 1695.9 1787.7 0.075951 1.1087e+05 67284 0.35363 0.63819 0.36181 0.72362 0.86416 True -ACD_m67694316 ACD 279.28/451.09 408.82/385.65 365.18 397.23 0.12105 7514.3 8238.3 0.35311 0.63799 0.36201 0.72402 0.86416 True -ACVR1C_m158401113 ACVR1C 329.25/328.55 306.61/410.25 328.9 358.43 0.12369 2685.5 7333.1 0.34487 0.63488 0.36512 0.73024 0.86924 True -ADCK3_m227149153 ADCK3 1240.6/2438.4 1870/2081.6 1839.5 1975.8 0.10307 3.6988e+05 1.5649e+05 0.34456 0.63479 0.36521 0.73043 0.86924 True -ACSS2_m33501214 ACSS2 680.55/747.67 533.1/1016.2 714.11 774.65 0.11723 59471 31405 0.3416 0.63366 0.36634 0.73268 0.86924 True -ACD_p67694285 ACD 608.53/495.49 551.4/630.54 552.01 590.97 0.098226 4760.4 13045 0.34114 0.6335 0.3665 0.733 0.86924 True -ADI1_p3517660 ADI1 980.4/834.7 943.81/769.01 907.55 856.41 -0.083578 12946 22683 0.33954 0.3671 0.6329 0.7342 0.86924 False -A1CF_p52596023 A1CF 286.62/761.88 493.99/442.87 524.25 468.43 -0.16211 57120 27252 0.33943 0.36714 0.63286 0.73429 0.86924 False -ADARB1_p46595724 ADARB1 343.95/129.64 305.98/227.16 236.8 266.57 0.17018 13035 7737.4 0.33846 0.63118 0.36882 0.73764 0.87104 True -ADRB2_m148206406 ADRB2 829.01/861.33 992.39/795.33 845.17 893.86 0.080714 9969.2 20955 0.33635 0.6317 0.3683 0.7366 0.87085 True -AES_p3061179 AES 268.99/310.79 112.93/397.67 289.89 255.3 -0.18265 20706 11150 0.33263 0.36971 0.63029 0.73942 0.87211 False -ABTB1_p127395834 ABTB1 502.7/861.33 413.86/825.09 682.02 619.47 -0.13855 74431 35814 0.33073 0.37042 0.62958 0.74085 0.87233 False -ADPRHL2_p36554520 ADPRHL2 1102.4/1054.9 1372.8/910.34 1078.7 1141.6 0.081719 54035 36338 0.33007 0.62933 0.37067 0.74135 0.87233 True -ACTN1_p69445679 ACTN1 257.23/131.42 240.37/191.11 194.32 215.74 0.15009 4563.5 4244.2 0.32872 0.62828 0.37172 0.74343 0.87279 True -ADARB1_p46595700 ADARB1 739.35/914.61 992.39/755.85 826.98 874.12 0.079888 21667 20858 0.32641 0.62794 0.37206 0.74411 0.87279 True -ADAM12_m128076635 ADAM12 355.71/708.6 520.48/635.69 532.16 578.09 0.11923 34452 19833 0.32616 0.62782 0.37218 0.74437 0.87279 True -ADCK1_m78285376 ADCK1 1459.6/1179.2 1296.5/1222.2 1319.4 1259.3 -0.067181 21030 34400 0.32391 0.373 0.627 0.746 0.87369 False -AAK1_p69870063 AAK1 485.06/296.58 229.64/651.71 390.82 440.68 0.17281 53417 23728 0.32368 0.62481 0.37519 0.75038 0.87676 True -ACAD9_p128598551 ACAD9 1159.7/1335.5 1120.5/1258.8 1247.6 1189.6 -0.06861 12509 32323 0.32255 0.37352 0.62648 0.74703 0.87387 False -AES_p3061195 AES 539.44/355.19 834.67/193.97 447.32 514.32 0.20095 1.1111e+05 43919 0.31971 0.61916 0.38084 0.76168 0.88069 True -ADCK2_p140373122 ADCK2 579.13/932.37 760.85/653.43 755.75 707.14 -0.095779 34080 23695 0.31578 0.37609 0.62391 0.75217 0.87782 False -AFF1_p87967869 AFF1 718.77/1246.7 1205.6/897.18 982.74 1051.4 0.09734 93468 47678 0.31446 0.62341 0.37659 0.75317 0.87797 True -ACLY_p40070105 ACLY 761.39/475.95 559.6/596.79 618.67 578.19 -0.097466 20715 16778 0.31253 0.37732 0.62268 0.75464 0.8784 False -ADRM1_p60878671 ADRM1 420.38/134.97 307.87/312.41 277.68 310.14 0.15898 20370 10840 0.31182 0.62096 0.37904 0.75808 0.87899 True -ACP1_p272185 ACP1 1625.7/797.4 951.38/1287.4 1211.5 1119.4 -0.11402 1.9974e+05 87436 0.31165 0.37765 0.62235 0.75531 0.8784 False -ABHD14B_p52004124 ABHD14B 858.4/969.67 791.14/1137.5 914.04 964.32 0.077173 33086 26271 0.31021 0.6218 0.3782 0.7564 0.87866 True -ADCK5_m145603095 ADCK5 906.91/1017.6 848.55/1175.3 962.26 1011.9 0.072493 29749 26056 0.30752 0.62078 0.37922 0.75845 0.87899 True -AHNAK2_m105444555 AHNAK2 504.17/277.05 162.14/738.11 390.61 450.13 0.20413 95833 37863 0.30588 0.61147 0.38853 0.77707 0.88952 True -ACTL6A_p179291144 ACTL6A 185.2/449.31 146.37/412.54 317.26 279.45 -0.18244 35151 16414 0.30587 0.37985 0.62015 0.7597 0.87942 False -ACTL7A_p111624686 ACTL7A 341.01/118.99 105.99/296.96 230 201.48 -0.19014 21441 10431 0.29891 0.38251 0.61749 0.76501 0.88352 False -ACVR2A_m148653961 ACVR2A 862.81/431.56 651.08/545.86 647.18 598.47 -0.11272 49264 26802 0.29763 0.38299 0.61701 0.76599 0.88363 False -AEBP2_p19615492 AEBP2 730.53/387.16 403.77/822.22 558.84 613 0.13321 73252 33234 0.29707 0.61638 0.38362 0.76725 0.88386 True -AES_p3056318 AES 1078.9/658.88 797.44/839.39 868.88 818.42 -0.08622 44542 29254 0.29504 0.38398 0.61602 0.76796 0.88386 False -ADAP1_m994106 ADAP1 440.96/660.65 675.05/498.94 550.81 587 0.091646 19820 15282 0.29275 0.61514 0.38486 0.76972 0.88486 True -ADRA1B_m159344006 ADRA1B 501.23/221.99 217.66/434.86 361.61 326.26 -0.14799 31287 15861 0.28397 0.38822 0.61178 0.77644 0.88952 False -AFMID_m76187079 AFMID 395.4/470.63 546.35/251.76 433.01 399.06 -0.11753 23111 14342 0.28378 0.38829 0.61171 0.77658 0.88952 False -ADNP2_p77891053 ADNP2 354.24/321.45 338.16/288.38 337.84 313.27 -0.10862 888.29 7555.2 0.2828 0.38866 0.61134 0.77733 0.88952 False -ADRA1A_p26722465 ADRA1A 555.61/497.27 957.69/225.44 526.44 591.57 0.16797 1.349e+05 53216 0.28232 0.60673 0.39327 0.78654 0.89485 True -ADAR_m154574165 ADAR 673.2/518.58 545.72/579.05 595.89 562.38 -0.083345 6254.9 14204 0.28113 0.3893 0.6107 0.77861 0.88997 False -ACTN1_m69445718 ACTN1 1086.2/632.24 1101.5/731.25 859.24 916.39 0.092804 85806 42831 0.27617 0.60878 0.39122 0.78243 0.8914 True -ACAT2_p160183121 ACAT2 1668.3/1926.9 2248.5/1506.6 1797.6 1877.5 0.062721 1.5434e+05 83802 0.27607 0.60875 0.39125 0.78249 0.8914 True -ACVRL1_m52306307 ACVRL1 818.72/976.77 1041.6/836.53 897.74 939.06 0.064848 16759 22410 0.27601 0.60873 0.39127 0.78254 0.8914 True -AGAP2_m58129192 AGAP2 795.2/726.36 1025.2/378.78 760.78 701.99 -0.11587 1.0565e+05 47642 0.26974 0.39368 0.60632 0.78736 0.89485 False -ACTR3_p114674498 ACTR3 1317/1179.2 1340.6/1059.1 1248.1 1199.9 -0.056823 24561 32337 0.26827 0.39424 0.60576 0.78849 0.89511 False -AHNAK_m62303507 AHNAK 116.12/236.2 292.73/19.454 176.16 156.09 -0.17343 22275 9866.6 0.26114 0.39699 0.60301 0.79398 0.89922 False -ADH7_m100349694 ADH7 811.37/1031.8 1026.5/896.04 921.6 961.25 0.060706 16403 23074 0.26102 0.60296 0.39704 0.79407 0.89922 True -ADAP1_p975103 ADAP1 909.85/710.38 528.05/1215.3 810.11 871.68 0.10555 1.2803e+05 56003 0.26017 0.60251 0.39749 0.79498 0.89922 True -ADK_p75960554 ADK 363.06/825.82 736.88/349.6 594.44 543.24 -0.1297 91032 39787 0.25891 0.39785 0.60215 0.7957 0.89922 False -AHCY_m32883330 AHCY 2009.3/2235.9 2437.1/1938 2122.6 2187.6 0.043454 75125 63974 0.25674 0.60131 0.39869 0.79738 0.9001 True -AGFG1_m228337178 AGFG1 549.73/1021.2 786.72/874.87 785.45 830.79 0.080867 57506 32045 0.25329 0.59998 0.40002 0.80005 0.90139 True -ADAR_p154574043 ADAR 765.8/1046 1073.8/821.08 905.92 947.43 0.064565 35596 26957 0.25282 0.5998 0.4002 0.80041 0.90139 True -ADH5_p100003224 ADH5 555.61/362.29 341.94/643.13 458.95 492.54 0.10167 32022 17756 0.25204 0.59938 0.40062 0.80124 0.90139 True -ACTL6B_m100253971 ACTL6B 483.59/458.19 227.75/640.84 470.89 434.3 -0.11645 42822 21561 0.25025 0.4012 0.5988 0.80239 0.90168 False -ADAMTS5_p28338612 ADAMTS5 114.65/294.81 175.39/274.07 204.73 224.73 0.13386 10549 6401.9 0.24999 0.59658 0.40342 0.80683 0.90361 True -ADARB1_m46595675 ADARB1 568.84/470.63 704.7/398.81 519.73 551.76 0.086102 25804 16734 0.24756 0.59775 0.40225 0.8045 0.90303 True -AEBP1_m44144302 AEBP1 590.89/848.9 495.88/861.13 719.9 678.51 -0.085304 49995 28351 0.24583 0.40291 0.59709 0.80582 0.90349 False -AGTPBP1_m88296222 AGTPBP1 1672.7/903.96 1204.4/1508.8 1288.3 1356.6 0.074437 1.7092e+05 79307 0.24242 0.59577 0.40423 0.80845 0.90386 True -AEN_p89169534 AEN 1030.4/1065.6 1486.4/452.02 1048 969.2 -0.11263 2.6778e+05 1.0701e+05 0.24185 0.40445 0.59555 0.80889 0.90386 False -AGPAT5_m6566254 AGPAT5 764.33/475.95 369.7/785.6 620.14 577.65 -0.10223 64035 31244 0.24073 0.40488 0.59512 0.80976 0.90386 False -ADAD1_p123301373 ADAD1 923.08/836.47 918.58/770.16 879.78 844.37 -0.059198 7382.3 21912 0.23921 0.40547 0.59453 0.81094 0.90416 False -ADH5_m100009841 ADH5 1943.2/2260.8 2097.1/1992.9 2102 2045 -0.039631 27932 57767 0.23708 0.4063 0.5937 0.81259 0.90499 False -ADRB3_m37823842 ADRB3 142.58/0 0/138.47 71.289 69.234 -0.041603 9875.4 4185 0.23486 0.40716 0.59284 0.81431 0.90538 False -AFF2_p147891422 AFF2 122/626.91 374.12/303.26 374.45 338.69 -0.14443 64989 27310 0.23431 0.40737 0.59263 0.81475 0.90538 False -ADAP1_m994101 ADAP1 1148/797.4 909.11/1116.3 972.68 1012.7 0.058131 41459 30154 0.23055 0.59117 0.40883 0.81767 0.90609 True -ADH7_p100350724 ADH7 342.48/385.38 449.19/320.42 363.93 384.81 0.08026 4605.7 8206.9 0.23045 0.59112 0.40888 0.81776 0.90609 True -ACVR1C_p158443786 ACVR1C 1409.6/918.16 1267.5/1152.9 1163.9 1210.2 0.056251 63657 41164 0.22828 0.59029 0.40971 0.81943 0.90609 True -ABI1_p27149695 ABI1 232.24/76.366 152.68/185.39 154.3 169.03 0.13072 6341.7 4221 0.2267 0.58604 0.41396 0.82792 0.9069 True -ADCK2_m140373231 ADCK2 1314.1/1149 1178.5/1203.9 1231.6 1191.2 -0.048039 6969.3 31860 0.22615 0.41054 0.58946 0.82109 0.90609 False -ACAD11_p132378525 ACAD11 818.72/1150.8 718.58/1158.7 984.77 938.63 -0.06916 75990 41891 0.22544 0.41082 0.58918 0.82164 0.90609 False -ADARB1_m46595697 ADARB1 712.89/566.53 600.61/623.11 639.71 611.86 -0.064117 5481.9 15371 0.22465 0.41113 0.58887 0.82226 0.90609 False -ADAMTS5_p28338607 ADAMTS5 1114.2/941.25 1267.5/690.05 1027.7 978.75 -0.070341 90824 47641 0.22428 0.41127 0.58873 0.82254 0.90609 False -ADNP_m49518587 ADNP 3017.6/2642.6 1834.6/3573.3 2830.1 2703.9 -0.065775 7.9088e+05 3.1726e+05 0.22401 0.41137 0.58863 0.82275 0.90609 False -ACAD9_p128598525 ACAD9 574.72/531.01 271.28/929.22 552.86 600.25 0.11844 1.087e+05 44945 0.22353 0.58655 0.41345 0.82689 0.9069 True -ABL2_m179100544 ABL2 649.68/268.17 547.61/301.54 458.93 424.58 -0.11198 51526 24257 0.22298 0.41178 0.58822 0.82355 0.90609 False -ADH7_p100349757 ADH7 605.59/575.41 513.54/719.23 590.5 616.39 0.061807 10805 14061 0.21835 0.58642 0.41358 0.82716 0.9069 True -AFF3_p100623814 AFF3 949.54/1074.4 610.7/1307.4 1012 959.07 -0.077415 1.2526e+05 58824 0.21824 0.41362 0.58638 0.82725 0.9069 False -ACTR5_p37377163 ACTR5 532.09/822.26 991.13/451.45 677.18 721.29 0.090912 93862 42205 0.21472 0.5848 0.4152 0.83039 0.90861 True -ABCB8_p150730705 ABCB8 1092.1/927.04 883.25/1204.4 1009.6 1043.8 0.048104 32604 27893 0.20516 0.58128 0.41872 0.83745 0.91533 True -ADRB1_m115803996 ADRB1 645.27/515.02 705.97/400.53 580.15 553.25 -0.068381 27564 18379 0.19845 0.42134 0.57866 0.84269 0.92005 False -AHCTF1_m247067252 AHCTF1 196.96/301.91 423.96/34.331 249.44 229.14 -0.12191 40706 17163 0.19677 0.422 0.578 0.84401 0.92049 False -ACTR3_p114684953 ACTR3 85.253/635.79 181.07/624.25 360.52 402.66 0.15905 1.2488e+05 47040 0.19428 0.55559 0.44441 0.88882 0.94234 True -ADRM1_p60878687 ADRM1 202.84/319.67 429.01/51.496 261.26 240.25 -0.12044 39040 16798 0.19427 0.42298 0.57702 0.84596 0.92161 False -ABLIM2_p8108331 ABLIM2 1300.8/1157.9 1311.6/1078.6 1229.4 1195.1 -0.040773 18685 31797 0.19227 0.42376 0.57624 0.84753 0.92205 False -ACTL7A_m111624726 ACTL7A 405.68/303.69 417.02/257.48 354.69 337.25 -0.072514 8963.8 8304.8 0.1914 0.42411 0.57589 0.84821 0.92205 False -AFF1_m87967382 AFF1 371.88/348.09 144.47/526.41 359.98 335.44 -0.10157 36610 17608 0.18984 0.42472 0.57528 0.84944 0.92238 False -ACTR8_m53916111 ACTR8 651.15/1081.6 576/1072.3 866.35 824.14 -0.071988 1.0788e+05 50320 0.18828 0.42533 0.57467 0.85066 0.9227 False -ACD_p67694362 ACD 1590.4/1097.5 1333.1/1433.3 1344 1383.2 0.041472 63241 44489 0.18596 0.57376 0.42624 0.85247 0.92307 True -ADRA1B_m159344028 ADRA1B 1074.5/527.46 930.56/746.12 800.97 838.34 0.06572 83312 40930 0.18475 0.57327 0.42673 0.85346 0.92307 True -ADNP2_p77875445 ADNP2 655.56/619.81 562.12/667.74 637.68 614.93 -0.052338 3108.2 15316 0.18386 0.42706 0.57294 0.85412 0.92307 False -AGPAT5_m6566223 AGPAT5 1149.4/2033.5 1704/1361.2 1591.4 1532.6 -0.054301 2.2475e+05 1.0317e+05 0.18313 0.42735 0.57265 0.85469 0.92307 False -ADCK2_m140373168 ADCK2 689.37/548.77 447.93/740.97 619.07 594.45 -0.05844 26411 18683 0.18009 0.42854 0.57146 0.85708 0.92465 False -ADCK1_p78285386 ADCK1 809.9/687.29 678.84/865.71 748.6 772.27 0.044868 12489 18308 0.175 0.56946 0.43054 0.86108 0.92796 True -AEBP1_m44144338 AEBP1 580.6/351.64 693.35/293.53 466.12 493.44 0.082003 53070 24895 0.17315 0.56806 0.43194 0.86389 0.92909 True -ADD1_p2877761 ADD1 1411.1/648.22 1070/1076.8 1029.6 1073.4 0.060002 1.455e+05 65902 0.1705 0.56768 0.43232 0.86465 0.92909 True -ADCY1_p45614255 ADCY1 451.25/825.82 820.16/515.54 638.53 667.85 0.064655 58273 29651 0.17023 0.56754 0.43246 0.86492 0.92909 True -ACRC_p70800708 ACRC 2395.9/2441.9 2463/2461.5 2418.9 2462.3 0.025616 530.42 67543 0.1668 0.56624 0.43376 0.86753 0.93089 True -ADAD1_p123301349 ADAD1 956.89/285.93 505.97/664.87 621.41 585.42 -0.085913 1.1886e+05 49541 0.16543 0.4343 0.5657 0.86861 0.93105 False -ADRB2_p148206418 ADRB2 443.9/412.02 461.81/361.62 427.96 411.71 -0.055699 2763.7 9828.1 0.16388 0.43491 0.56509 0.86983 0.93136 False -AGAP2_p58129153 AGAP2 305.73/204.23 254.25/279.8 254.98 267.02 0.066304 2738.7 5525.1 0.16197 0.5642 0.4358 0.8716 0.93225 True -ACVR2A_m148602767 ACVR2A 1593.3/1419 1124.2/1805.2 1506.2 1464.7 -0.040206 1.2354e+05 67752 0.15914 0.43678 0.56322 0.87356 0.93336 False -ADAD1_m123301295 ADAD1 595.3/887.97 957.69/578.48 741.64 768.08 0.050485 57366 31201 0.14973 0.5595 0.4405 0.88099 0.94029 True -ACO2_m41895824 ACO2 590.89/333.88 292.1/679.75 462.38 485.93 0.071499 54082 25168 0.1484 0.5582 0.4418 0.8836 0.94106 True -ACTL7A_m111624641 ACTL7A 1931.4/1555.7 1583.5/1838.4 1743.6 1711 -0.027209 51526 48451 0.14808 0.44114 0.55886 0.88228 0.94066 False -ABL2_p179100489 ABL2 2303.3/1491.8 1720.4/1986.6 1897.5 1853.5 -0.033842 1.8234e+05 95146 0.1427 0.44326 0.55674 0.88653 0.94234 False -ADCK2_p140373158 ADCK2 837.83/953.68 861.16/972.71 895.76 916.94 0.03368 6466.2 22355 0.14167 0.55633 0.44367 0.88734 0.94234 True -ABL1_m133729594 ABL1 780.5/502.59 1136.9/78.389 641.55 607.63 -0.078246 2.994e+05 1.1008e+05 0.13952 0.44452 0.55548 0.88904 0.94234 False -ACVR1_m158637066 ACVR1 709.95/658.88 675.05/658.01 684.41 666.53 -0.038138 724.69 16570 0.13891 0.44476 0.55524 0.88952 0.94234 False -AHNAK_m62303523 AHNAK 179.32/298.36 150.15/305.54 238.84 227.85 -0.067691 9579.1 6618 0.13747 0.44533 0.55467 0.89066 0.94255 False -ACTN1_m69445724 ACTN1 1471.3/1317.8 1537.5/1199.9 1394.5 1368.7 -0.027001 34393 36587 0.13528 0.4462 0.5538 0.89239 0.94308 False -ADK_p76074481 ADK 438.02/74.59 254.88/231.16 256.31 243.02 -0.076482 33161 14758 0.13372 0.44681 0.55319 0.89362 0.94308 False -AAK1_m69870131 AAK1 1108.3/1355 890.19/1508.8 1231.7 1199.5 -0.038129 1.1091e+05 58211 0.13326 0.44699 0.55301 0.89399 0.94308 False -AFAP1L2_m116093045 AFAP1L2 708.48/847.13 752.65/767.87 777.8 760.26 -0.032869 4863.8 19105 0.12692 0.4495 0.5505 0.899 0.94737 False -ACAD11_p132378501 ACAD11 1065.7/742.35 903.43/865.71 904 884.57 -0.03131 26488 23886 0.12571 0.44998 0.55002 0.89996 0.94737 False -AHRR_p344030 AHRR 736.41/893.3 1114.8/459.46 814.85 787.12 -0.049891 1.1352e+05 51252 0.12272 0.45117 0.54883 0.90233 0.94887 False -AATK_p79104858 AATK 417.44/614.48 570.96/487.5 515.96 529.23 0.036555 11447 12101 0.1206 0.54799 0.45201 0.90401 0.94964 True -AAAS_m53715238 AAAS 47.036/239.75 265.6/44.058 143.39 154.83 0.10997 21556 9127.4 0.11971 0.51532 0.48468 0.96936 0.97895 True -ADRB2_p148206455 ADRB2 4950.5/3555.4 3992.9/4392.1 4253 4192.5 -0.020666 5.2639e+05 2.5987e+05 0.11869 0.45276 0.54724 0.90552 0.95023 False -AATF_p35307527 AATF 2185.7/1482.9 1741.9/1862.5 1834.3 1802.2 -0.025487 1.2711e+05 75463 0.11699 0.45343 0.54657 0.90687 0.95064 False -ADCK5_m145603170 ADCK5 858.4/802.73 1068.7/548.15 830.57 808.44 -0.03891 68525 36543 0.11576 0.45392 0.54608 0.90784 0.95066 False -ACAD11_m132378519 ACAD11 401.27/440.43 453.61/365.62 420.85 409.62 -0.038954 2318.8 9646.8 0.11443 0.45445 0.54555 0.9089 0.95077 False -ADRM1_m60878679 ADRM1 1584.5/1610.8 1700.9/1448.8 1597.7 1574.8 -0.020753 16063 42565 0.11067 0.45594 0.54406 0.91188 0.9529 False -ACVR1B_m52369226 ACVR1B 51.445/364.07 324.91/117.3 207.76 221.1 0.0894 35209 14669 0.11019 0.5233 0.4767 0.95339 0.97432 True -ADCK5_p145603142 ADCK5 1280.3/1479.4 1263/1536.3 1379.8 1399.7 0.020605 28579 36157 0.10446 0.5416 0.4584 0.9168 0.95704 True -ADRA1A_m26722473 ADRA1A 811.37/580.73 739.4/625.97 696.05 682.68 -0.027935 16515 16884 0.10287 0.45903 0.54097 0.91806 0.95726 False -ABL1_m133729467 ABL1 984.81/797.4 1328/394.23 891.11 861.13 -0.049315 2.2677e+05 90408 0.10177 0.45947 0.54053 0.91894 0.95726 False -ADARB2_m1779266 ADARB2 1568.4/2083.2 1810.7/1887.6 1825.8 1849.1 0.01834 67745 55503 0.099199 0.53951 0.46049 0.92098 0.95824 True -AEN_m89169495 AEN 662.91/594.94 834.04/391.94 628.93 612.99 -0.036969 50016 26727 0.097564 0.46114 0.53886 0.92228 0.95824 False -AGTPBP1_p88307644 AGTPBP1 302.79/335.65 274.44/347.89 319.22 311.16 -0.036787 1618.7 7093.6 0.095825 0.46183 0.53817 0.92366 0.95824 False -AGBL5_m27275975 AGBL5 779.03/932.37 778.52/905.19 855.7 841.85 -0.02351 9889.9 21246 0.095004 0.46216 0.53784 0.92431 0.95824 False -ABHD14B_m52004106 ABHD14B 446.84/394.26 519.22/340.45 420.55 429.84 0.03143 8681.2 9639 0.094568 0.53767 0.46233 0.92467 0.95824 True -ABTB1_m127395808 ABTB1 1184.7/650 888.29/910.91 917.36 899.6 -0.028163 71609 39174 0.089702 0.46426 0.53574 0.92852 0.96124 False -ADRB1_m115804041 ADRB1 414.5/1147.3 604.39/1002.5 780.88 803.43 0.041009 1.7385e+05 70742 0.084761 0.533 0.467 0.934 0.96456 True -ACO2_m41903807 ACO2 474.77/1126 864.95/773.02 800.36 818.98 0.033145 1.0812e+05 49189 0.083972 0.53339 0.46661 0.93322 0.96456 True -AFTPH_m64778724 AFTPH 49.976/127.87 105.99/66.945 88.922 86.467 -0.039928 1897.9 1774.5 0.081538 0.46751 0.53249 0.93501 0.96456 False -ADCK3_m227149144 ADCK3 263.11/168.72 189.27/231.73 215.91 210.5 -0.036444 2678.3 4592 0.080811 0.4678 0.5322 0.93559 0.96456 False -ABCF1_p30545888 ABCF1 264.58/353.41 442.25/191.68 309 316.97 0.036632 17670 10451 0.077983 0.53049 0.46951 0.93902 0.9661 True -ADD3_p111860411 ADD3 593.83/834.7 523.01/932.66 714.26 727.83 0.027114 56457 30403 0.077825 0.53101 0.46899 0.93799 0.96603 True -ADARB2_m1779272 ADARB2 1018.6/513.25 679.47/824.51 765.93 751.99 -0.026472 69110 35557 0.07398 0.47051 0.52949 0.94103 0.96717 False -ADRA1B_m159343921 ADRA1B 67.614/245.08 196.21/110.43 156.35 153.32 -0.028039 9713 5375.9 0.063038 0.47487 0.52513 0.94974 0.97432 False -ACTR3_p114688931 ACTR3 721.71/768.99 584.83/888.6 745.35 736.72 -0.016779 23627 20022 0.060988 0.47568 0.52432 0.95137 0.97432 False -AAAS_p53714391 AAAS 415.97/708.6 601.87/537.85 562.29 569.86 0.019263 22433 16355 0.059205 0.5236 0.4764 0.95279 0.97432 True -ADARB1_m46595715 ADARB1 749.63/378.28 472.54/673.46 563.96 573 0.022906 44569 23763 0.058653 0.52333 0.47667 0.95335 0.97432 True -ADI1_p3523171 ADI1 636.45/156.28 564.02/250.04 396.37 407.03 0.038194 82286 33445 0.058293 0.51593 0.48407 0.96814 0.97895 True -AGPAT3_m45379570 AGPAT3 127.88/230.87 191.16/160.78 179.38 175.97 -0.027492 2882.6 3736.7 0.057892 0.47692 0.52308 0.95383 0.97432 False -AHRR_p344042 AHRR 1230.3/525.68 837.19/945.24 877.98 891.22 0.021562 1.2703e+05 56920 0.055478 0.52207 0.47793 0.95587 0.9754 True -AEN_m89169500 AEN 679.08/568.3 741.3/492.65 623.69 616.97 -0.015604 18524 16137 0.052901 0.47891 0.52109 0.95781 0.976 False -ADCK4_m41220523 ADCK4 565.9/776.09 878.2/445.73 670.99 661.96 -0.01952 57802 30074 0.052147 0.47921 0.52079 0.95841 0.976 False -AEBP2_p19615544 AEBP2 745.22/751.23 736.25/773.59 748.23 754.92 0.012832 357.59 18298 0.049483 0.51973 0.48027 0.96053 0.97716 True -AEBP1_p44144359 AEBP1 770.21/880.87 577.9/1056.8 825.54 817.36 -0.014356 60403 33744 0.044558 0.48223 0.51777 0.96446 0.97895 False -AHCTF1_p247079429 AHCTF1 601.18/900.41 735.62/754.13 750.79 744.88 -0.011396 22470 19735 0.042105 0.48321 0.51679 0.96641 0.97895 False -ADNP2_p77875499 ADNP2 745.22/614.48 453.61/920.64 679.85 687.12 0.015331 58803 30566 0.041603 0.51657 0.48343 0.96686 0.97895 True -ACTR5_m37377171 ACTR5 438.02/443.99 482/391.94 441 436.97 -0.013222 2036.4 10162 0.040007 0.48404 0.51596 0.96809 0.97895 False -ABTB1_m127395860 ABTB1 945.13/1102.9 885.77/1174.7 1024 1030.2 0.0087472 27089 26326 0.038419 0.51532 0.48468 0.96935 0.97895 True -ACTL6B_m100253168 ACTL6B 751.1/939.48 471.91/1238.2 845.29 855.05 0.016548 1.5567e+05 65863 0.038041 0.51493 0.48507 0.97013 0.97895 True -AEBP2_m19615572 AEBP2 1023/468.85 729.31/774.73 745.94 752.02 0.011697 77295 37922 0.031226 0.51242 0.48758 0.97515 0.98302 True -AFF1_p87967890 AFF1 824.6/1007 526.79/1316.6 915.78 921.69 0.0092723 1.6426e+05 70028 0.022338 0.50878 0.49122 0.98244 0.98938 True -ACHE_p100491656 ACHE 2626.7/1706.7 1749.5/2570.2 2166.7 2159.8 -0.0045493 3.8001e+05 1.665e+05 0.016725 0.49333 0.50667 0.98666 0.9925 False -ACHE_m100491816 ACHE 295.44/420.9 380.43/338.73 358.17 359.58 0.005641 4369.4 8062.6 0.015671 0.50624 0.49376 0.98753 0.9925 True -ACHE_m100491779 ACHE 280.75/525.68 298.41/505.24 403.21 401.82 -0.0049676 25692 14696 0.012018 0.49521 0.50479 0.99041 0.99439 False -AGAP2_m58128444 AGAP2 2459.1/1609 1964.6/2109.6 2034.1 2037.1 0.0021679 1.8592e+05 99102 0.0097214 0.50388 0.49612 0.99224 0.99523 True -ADRB2_p148206450 ADRB2 608.53/877.32 955.17/533.27 742.92 744.22 0.0025145 62561 32956 0.0071485 0.50284 0.49716 0.99432 0.99631 True -ACVRL1_m52306881 ACVRL1 1665.4/1102.9 1593/1178.1 1384.1 1385.6 0.0015047 1.2213e+05 64899 0.0056737 0.50226 0.49774 0.99547 0.99647 True -ACAD11_m132378564 ACAD11 989.22/1545.1 1045.4/1487.1 1267.1 1266.2 -0.0010322 1.2602e+05 63931 0.0035873 0.49857 0.50143 0.99714 0.99714 False
--- a/test-data/output.count_normalized.txt Thu Apr 19 05:34:53 2018 -0400 +++ b/test-data/output.count_normalized.txt Mon Jun 04 10:58:04 2018 -0400 @@ -1,4 +1,4 @@ -sgRNA Gene test1_fastq_gz +sgRNA Gene test1.fastq.gz s_47512 RNF111 2.0 s_24835 HCFC1R1 2.0 s_14784 CYP4B1 8.0
--- a/test-data/output_summary.Rnw Thu Apr 19 05:34:53 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,1063 +0,0 @@ -% This is a template file for Sweave used in MAGeCK -% Author: Wei Li, Shirley Liu lab -% Do not modify lines beginning with "#__". -\documentclass{article} - -\usepackage{amsmath} -\usepackage{amscd} -\usepackage[tableposition=top]{caption} -\usepackage{ifthen} -\usepackage{fullpage} -\usepackage[utf8]{inputenc} - -\begin{document} -\setkeys{Gin}{width=0.9\textwidth} - -\title{MAGeCK Comparison Report} -\author{Wei Li} - -\maketitle - - -\tableofcontents - -\section{Summary} - -%Function definition -<<label=funcdef,include=FALSE,echo=FALSE>>= -genreporttable<-function(comparisons,ngenes,direction,fdr1,fdr5,fdr25){ - xtb=data.frame(Comparison=comparisons,Genes=ngenes,Selection=direction,FDR1=fdr1,FDR5=fdr5,FDR25=fdr25); - colnames(xtb)=c("Comparison","Genes","Selection","FDR1%","FDR5%","FDR25%"); - return (xtb); -} -@ - -The statistics of comparisons is as indicated in the following table. - -<<label=tab1,echo=FALSE,results=tex>>= -library(xtable) -comparisons=c("HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.","HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos."); -ngenes=c(100,100); -direction=c("negative","positive"); -fdr1=c(0,0); -fdr5=c(2,0); -fdr25=c(9,1); - -cptable=genreporttable(comparisons,ngenes,direction,fdr1,fdr5,fdr25); -print(xtable(cptable, caption = "Summary of comparisons", label = "tab:one", - digits = c(0, 0, 0, 0, 0, 0, 0), - table.placement = "tbp", - caption.placement = "top")) -@ - -The meanings of the columns are as follows. - -\begin{itemize} -\item \textbf{Comparison}: The label for comparisons; -\item \textbf{Genes}: The number of genes in the library; -\item \textbf{Selection}: The direction of selection, either positive selection or negative selection; -\item \textbf{FDR1\%}: The number of genes with FDR $<$ 1\%; -\item \textbf{FDR5\%}: The number of genes with FDR $<$ 5\%; -\item \textbf{FDR25\%}: The number of genes with FDR $<$ 25\%; -\end{itemize} - -The following figures show: - -\begin{itemize} -\item Individual sgRNA read counts of selected genes in selected samples; -\item The distribution of RRA scores and p values of all genes; and -\item The RRA scores and p values of selected genes. -\end{itemize} - - -\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg.} - -The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg., and the RRA scores of 10 genes. - -<<echo=FALSE>>= -gstable=read.table('output.gene_summary.txt',header=T) -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=3 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1") -# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - -@ -%% -\clearpage -\newpage -The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples. -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285)) -targetgene="ACIN1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424)) -targetgene="ACTR8" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344)) -targetgene="AHCY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021)) -targetgene="ACLY" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173)) -targetgene="AATF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033)) -targetgene="AGBL5" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991)) -targetgene="AHCTF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427)) -targetgene="ABT1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338)) -targetgene="ADIRF" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927)) -targetgene="ABCF1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ - -\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos.} - -The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos., and the RRA scores of 10 genes. - -<<echo=FALSE>>= -gstable=read.table('output.gene_summary.txt',header=T) -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# -# -# parameters -# Do not modify the variables beginning with "__" - -# gstablename='__GENE_SUMMARY_FILE__' -startindex=9 -# outputfile='__OUTPUT_FILE__' -targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP") -# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]); -samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.' - - -# You need to write some codes in front of this code: -# gstable=read.table(gstablename,header=T) -# pdf(file=outputfile,width=6,height=6) - - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - -###### -# function definition - -plotrankedvalues<-function(val, tglist, ...){ - - plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...) - if(length(tglist)>0){ - for(i in 1:length(tglist)){ - targetgene=tglist[i]; - tx=which(names(val)==targetgene);ty=val[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - # text(tx+50,ty,targetgene,col=colors[i]) - } - legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors) - } -} - - - -plotrandvalues<-function(val,targetgenelist, ...){ - # choose the one with the best distance distribution - - mindiffvalue=0; - randval=val; - for(i in 1:20){ - randval0=sample(val) - vindex=sort(which(names(randval0) %in% targetgenelist)) - if(max(vindex)>0.9*length(val)){ - # print('pass...') - next; - } - mindiffind=min(diff(vindex)); - if (mindiffind > mindiffvalue){ - mindiffvalue=mindiffind; - randval=randval0; - # print(paste('Diff: ',mindiffvalue)) - } - } - plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...) - - if(length(targetgenelist)>0){ - for(i in 1:length(targetgenelist)){ - targetgene=targetgenelist[i]; - tx=which(names(randval)==targetgene);ty=randval[targetgene]; - points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20) - text(tx+50,ty,targetgene,col=colors[i]) - } - } - -} - - - - -# set.seed(1235) - - - -pvec=gstable[,startindex] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel)) - - -pvec=gstable[,startindex+1] -names(pvec)=gstable[,'id'] -pvec=sort(pvec); - -plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - -# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel)) - - - -# you need to write after this code: -# dev.off() - - - - -@ -%% -\clearpage -\newpage -The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples. -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192)) -targetgene="ACRC" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882)) -targetgene="AGAP3" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471)) -targetgene="ADCK4" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885)) -targetgene="AHRR" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979)) -targetgene="ADRBK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076)) -targetgene="ADK" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816)) -targetgene="ADCK1" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512)) -targetgene="ADARB2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -% - - -<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>= -par(mfrow=c(2,2)); - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832)) -targetgene="ACSS2" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -# parameters -# Do not modify the variables beginning with "__" -targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625)) -targetgene="ADNP" -collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final") - -# set up color using RColorBrewer -#library(RColorBrewer) -#colors <- brewer.pal(length(targetgenelist), "Set1") - -colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#A65628", "#F781BF", - "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", - "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", - "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F") - - -## code - -targetmatvec=unlist(targetmat)+1 -yrange=range(targetmatvec[targetmatvec>0]); -# yrange[1]=1; # set the minimum value to 1 -for(i in 1:length(targetmat)){ - vali=targetmat[[i]]+1; - if(i==1){ - plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y') - axis(1,at=1:length(vali),labels=(collabel),las=2) - # lines(0:100,rep(1,101),col='black'); - }else{ - lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))]) - } -} - - - -par(mfrow=c(1,1)); -@ -%__INDIVIDUAL_PAGE__ - - - - - - - - - -\end{document} -