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annotate fisher_test.xml @ 3:4b76708c6b95 draft default tip

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/fisher_test commit afc67da70753a5808ade516f71e9bf133efb3268
author artbio
date Sat, 14 Oct 2023 22:59:13 +0000
parents 8052d037517f
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3
4b76708c6b95 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/fisher_test commit afc67da70753a5808ade516f71e9bf133efb3268
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1 <tool id="fishertest" name="Fisher's exact test" version="2.32.0+galaxy0">
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2 <description>on two gene hit lists</description>
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3 <requirements>
3
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4 <requirement type="package" version="2.32.0">bioconductor-qvalue</requirement>
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5 </requirements>
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6 <command><![CDATA[
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7 Rscript '$fisher_test' "\${GALAXY_SLOTS:-1}"
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8 ]]></command>
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9 <configfiles>
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10 <configfile name="fisher_test">
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11 <![CDATA[
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12 ## Setup R error handling to go to stderr
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13 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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14 options(warn=-1)
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15 suppressMessages(library(qvalue))
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16 library(parallel)
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17 args = commandArgs(trailingOnly = TRUE)
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18 slots = as.numeric(args[1])
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19 countsTable = read.delim("${input}", header=TRUE, check.names=FALSE, stringsAsFactor = TRUE)
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20 depth1 = sum(countsTable[,2])
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21 depth2 = sum(countsTable[,3])
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22 float_table=data.frame(countsTable[,2], countsTable[,3])
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23
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24 calc_pvalue <- function(row, depth1, depth2, ... ){
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25 thearray = array( c(row, (depth1 - row[1]), (depth2 - row[2])), dim=c(2,2))
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26 current_test = fisher.test( thearray )
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27 return(current_test\$p.value)
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28 }
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29
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30 cl <- makePSOCKcluster(slots)
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31 clusterExport(cl=cl, varlist=c("calc_pvalue", "depth1", "depth2"))
5f784a95ce13 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/fisher_test commit 7d954797362ea5de843d812cb823ac1dd869e1df
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32 ptm <- proc.time()
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33 p_val = parApply(cl, float_table, 1, function(x) calc_pvalue(x, depth1, depth2))
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34 stopCluster(cl)
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35 proc.time() - ptm
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36 p_val[p_val>1]=1
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37 p = qvalue(p_val)
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38 finalTable = cbind(countsTable, data.frame(p\$pvalues), data.frame(p\$qvalues))
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39 write.table ( finalTable, file = "${output}", row.names=FALSE, col.names=TRUE, quote= FALSE, dec = ".", sep = "\t", eol = "\n")
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40 ]]>
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41 </configfile>
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42 </configfiles>
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43 <inputs>
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44 <param name="input" type="data" format="tabular" label="gene hit lists, 2 samples"/>
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45 </inputs>
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46 <outputs>
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47 <data name="output" format="tabular" label="Fisher test p-values" />
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48 </outputs>
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49 <tests>
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50 <test>
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51 <param name="input" value="counts.tab" ftype="tabular"/>
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52 <output name="output" file="fisher.tab" ftype="tabular"/>
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53 </test>
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54 </tests>
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55 <help>
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56
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57 **What it does**
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58
3
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59 Runs Fisher's exact test for testing the null of independence of rows and columns
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60 in a contingency table of two columns.
0
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61
3
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62 p.pvalues: the chance of getting this data if it is independent between columns
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63 (false negative); the p-value.
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64
3
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65 q.qvalues: FDR (Faslse Detection Rate) adjusted p-values;
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66 a q-value of 0.05 implies that 5% of significant tests will result in false positives.
0
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67
3
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68 Be aware that this test does not take into account the biological noise
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69 that would be visible if replicates were available.
0
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72 </help>
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73 <citations>
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74 <citation type="doi">10.1111/1467-9868.00346</citation>
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75 </citations>
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77 </tool>