Mercurial > repos > pravs > protein_rna_correlation

comparison protein_rna_correlation.r @ 0:fc89f8c3b777 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload
author pravs
date Sun, 17 Jun 2018 04:20:06 -0400
parents
children 412403eec79c
comparison
equal deleted inserted replaced
-1:000000000000 0:fc89f8c3b777
1 #==================================================================================
2 # About the script
3 #==================================================================================
4 # Version: V1
5 # This script works for single sample only
6 # It takes GE (Gene Expression) and PE (Protein expression) data of one sample and perform correlation, regression analysis between PE and GE data
7 # Input data can be of tsv format
8 # Script also need a parameter or option file
9
10 #==================================================================================
11 # Dependencies
12 #==================================================================================
13 # Following R package has to be installed.
14 # data.table
15 # gplots
16 # MASS
17 # DMwR
18 # mgcv
19 # It can be installed by following R command in R session. e.g. install.packages("data.table")
20
21 #==================================================================================
22 # How to Run
23 #==================================================================================
24 # Rscript PE_GE_association_singleSample_V1.r <PE_file> <GE_file> <Option_file containing tool parameters> <Ensembl map file containing directory path> <outdir>
25
26 #==================================================================================
27 # Arguments
28 #==================================================================================
29 # Arg1. <PE file>: PE data (tsv format)
30 # Arg2. <GE file>: GE data (tsv format)
31 # Arg3. <Option file>: tsv format, key\tvalue
32 # Options are
33 # PE_idcolno: Column number of PE file containing protein IDs
34 # GE_idcolno: Column number of GE file containing transcript IDs
35 # PE_expcolno: Column number of PE file containing protein expression values
36 # GE_expcolno: Column number of GE file containing transcript expression values
37 # PE_idtype: protein id type. It can be either Uniprot or Ensembl or HGNC_symbol
38 # GE_idtype: transcript id type. At present it is only one type i.e. Ensembl or HGNC_symbol
39 # Organism: Organism
40 # writeMapUnmap: Whether to write mapped and unmapped data in input data format. It takes value as 1 or 0. If 1, mapped and unmapped data is written. Default is 1.
41 # doscale: Whether perform scaling to input data or not. If yet, abundance values are normalized by standard normalization. Default 1
42 # Arg4. <Ensembl map file containg directory>: Path to Ensembl map file containg directory e.g. /home/user/Ensembl/mapfiles
43 # Arg5. <Outdir>: output directory (e.g. /home/user/out1)
44
45 #==================================================================================
46 # Sample option file
47 #==================================================================================
48 #PE_idcolno 7
49 #GE_idcolno 1
50 #PE_expcolno 2
51 #GE_expcolno 3
52 #PE_idtype Ensembl
53 #GE_idtype Ensembl
54 #Organism mmusculus
55 #writeMapUnmap 1
56 #doscale 1
57
58 #==================================================================================
59 # Output
60 #==================================================================================
61 # The script outputs image and data folder along with Correlation_result.html and Result.log file
62 # Result.log: Log file
63 # Correlation_result.html; main result file in html format
64
65 # data folder contains following output files
66
67 # PE_abundance.tsv: 2 column tsv file containing mapped id and protein expression values
68 # GE_abundance.tsv: 2 column tsv file containing mapped id and transcript expression values
69
70 # If writeMapUnmap is 1 i.e. to write mapped and unmapped data, 4 additional file will be written
71 # PE_unmapped.tsv: Output format is same as input, PE unmapped data is written
72 # GE_unmapped.tsv: Output format is same as input, GE unmapped data is written
73 # PE_mapped.tsv: Output format is same as input, PE mapped data is written
74 # GE_mapped.tsv: Output format is same as input, GE mapped data is written
75
76 # PE_GE_influential_observation.tsv: Influential observations based on cook's distance
77 # PE_GE_kmeans_clusterpoints.txt: Observations clustered based on kmeans clustering. File contains cluster assignment of each observations
78
79 #==================================================================================
80 # ............................SCRIPT STARTS FROM HERE .............................
81 #==================================================================================
82 # Warning Off
83 oldw <- getOption("warn")
84 options(warn = -1)
85 #=============================================================================================================
86 # Functions
87 #=============================================================================================================
88 usage <- function()
89 {
90 cat("\n\n###########\nERROR: Rscript PE_GE_association_singleSample_V1.r <PE file> <GE file> <Option file containing tool parameters> <Ensembl map file containing directory path> <outdir>\n###########\n\n");
91 }
92
93 #=============================================================================================================
94 # Global variables
95 #=============================================================================================================
96 noargs = 13;
97
98 #=============================================================================================================
99 # Parse command line arguments in args object
100 #=============================================================================================================
101 args = commandArgs(trailingOnly = TRUE);
102
103
104 #=============================================================================================================
105 # Check for No of arguments
106 #=============================================================================================================
107 if(length(args) != noargs)
108 {
109 usage();
110 stop(paste("Please check usage. Number of arguments is not equal to ",noargs,sep="",collapse=""));
111 }
112
113 #======================================================================
114 # Load libraries
115 #======================================================================
116 library(data.table, warn.conflicts=FALSE);
117 library(lattice, warn.conflicts=FALSE);
118 library(grid, warn.conflicts=FALSE);
119 library(nlme, warn.conflicts=FALSE);
120 library(gplots, warn.conflicts=FALSE);
121 library(MASS, warn.conflicts=FALSE);
122 library(DMwR, warn.conflicts=FALSE);
123 library(mgcv, warn.conflicts=FALSE);
124
125
126 #=============================================================================================================
127 # Set variables
128 #=============================================================================================================
129 #PE_file = args[1]; # Protein abundance data file
130 #GE_file = args[2]; # Gene expression data file
131 #option_file = args[3]; # Option file containing various parameters
132 #biomartdir = args[4]; # Biomart map file containing directory path in local system
133 #outdir = args[5]; # output directory
134
135 PE_file = args[1]; # Protein abundance data file
136 GE_file = args[2]; # Gene expression data file
137 #option_file = args[3]; # Option file containing various parameters
138 #biomartdir = args[4]; # Biomart map file containing directory path in local system
139 outdir = args[13]; # output directory
140
141 #imagesubdirprefix = "image";
142 #datasubdirprefix = "data";
143 #htmloutfile = "Correlation_result.html";
144 htmloutfile = args[12]
145
146 #logfile = "Result.log";
147 PE_outfile_mapped = "PE_mapped.tsv";
148 GE_outfile_mapped = "GE_mapped.tsv";
149 PE_outfile_unmapped = "PE_unmapped.tsv";
150 GE_outfile_unmapped = "GE_unmapped.tsv";
151 PE_expfile = "PE_abundance.tsv";
152 GE_expfile = "GE_abundance.tsv";
153 PE_outfile_excluded_naInf = "PE_excluded_NA_Inf.tsv";
154 GE_outfile_excluded_naInf = "GE_excluded_NA_Inf.tsv";
155
156 #=============================================================================================================
157 # Check input files existance
158 #=============================================================================================================
159 if(! file.exists(PE_file))
160 {
161 usage();
162 stop(paste("Input PE_file file does not exists. Path given: ",PE_file,sep="",collapse=""));
163 }
164 if(! file.exists(GE_file))
165 {
166 usage();
167 stop(paste("Input GE_file does not exists. Path given: ",GE_file,sep="",collapse=""));
168 }
169 #if(! file.exists(option_file))
170 #{
171 # usage();
172 # stop(paste("Input option_file does not exists. Path given: ",option_file,sep="",collapse=""));
173 #}
174
175 #=============================================================================================================
176 # Read param_file and set parameter/option data frame
177 #=============================================================================================================
178 #optiondf = read.table(option_file, header = FALSE, stringsAsFactors = FALSE)
179 #rownames(optiondf) = optiondf[,1];
180
181 #=============================================================================================================
182 # Define option variables
183 #=============================================================================================================
184 #PE_idcolno = as.numeric(optiondf["PE_idcolno",2]);
185 #GE_idcolno = as.numeric(optiondf["GE_idcolno",2]);
186 #PE_expcolno = as.numeric(optiondf["PE_expcolno",2]);
187 #GE_expcolno = as.numeric(optiondf["GE_expcolno",2]);
188 #PE_idtype = optiondf["PE_idtype",2];
189 #GE_idtype = optiondf["GE_idtype",2];
190 #Organism = optiondf["Organism",2];
191 #writeMapUnmap = as.logical(as.numeric(optiondf["writeMapUnmap",2]));
192 #doscale = as.logical(as.numeric(optiondf["doscale",2]));
193
194 PE_idcolno = as.numeric(args[3])
195 GE_idcolno = as.numeric(args[4])
196 PE_expcolno = as.numeric(args[5])
197 GE_expcolno = as.numeric(args[6])
198 PE_idtype = args[7]
199 GE_idtype = args[8]
200 #Organism = args[9]
201 writeMapUnmap = as.logical(as.numeric(args[10]));
202 doscale = as.logical(as.numeric(args[11]));
203
204
205 #1 PE_file = "test_data/PE_mouse_singlesample.txt"
206 #2 GE_file = "test_data/GE_mouse_singlesample.txt"
207 #3 PE_idcolno = 7
208 #4 GE_idcolno = 1
209 #5 PE_expcolno = 13
210 #6 GE_expcolno = 10
211 #7 PE_idtype = "Ensembl_with_version"
212 #8 GE_idtype = "Ensembl_with_version"
213 #10 writeMapUnmap = 1
214 #11 doscale = 1
215 #9 biomart_mapfile = "test_data/mmusculus_gene_ensembl__GRCm38.p6.map"
216 #12 htmloutfile = "html_out.html"
217 #13 outdir = "output_elements"
218
219 #=============================================================================================================
220 # Set column name of biomart map file (idtype) based on whether Ensembl or Uniprot
221 #=============================================================================================================
222 if(PE_idtype == "Ensembl")
223 {
224 PE_idtype = "ensembl_peptide_id";
225 }else
226 {
227 if(PE_idtype == "Ensembl_with_version")
228 {
229 PE_idtype = "ensembl_peptide_id_version";
230 }else{
231 if(PE_idtype == "HGNC_symbol")
232 {
233 PE_idtype = "hgnc_symbol";
234 }
235 }
236 }
237
238 if(GE_idtype == "Ensembl")
239 {
240 GE_idtype = "ensembl_transcript_id";
241 }else
242 {
243 if(GE_idtype == "Ensembl_with_version")
244 {
245 GE_idtype = "ensembl_transcript_id_version";
246 }else{
247 if(GE_idtype == "HGNC_symbol")
248 {
249 GE_idtype = "hgnc_symbol";
250 }
251 }
252 }
253 #=============================================================================================================
254 # Identify biomart mapping file
255 #=============================================================================================================
256 #biomartdir = gsub(biomartdir, pattern="/$", replacement="")
257 #biomart_mapfilename = list.files(path = biomartdir, pattern = Organism);
258 #biomart_mapfile = paste(biomartdir,"/",biomart_mapfilename,sep="",collapse="");
259 #print(biomart_mapfile);
260 biomart_mapfile = args[9];
261 #=============================================================================================================
262 # Parse PE, GE, biomart file file
263 #=============================================================================================================
264 PE_df = as.data.frame(fread(input=PE_file, header=T, sep="\t"));
265 GE_df = as.data.frame(fread(input=GE_file, header=T, sep="\t"));
266 biomart_df = as.data.frame(fread(input=biomart_mapfile, header=T, sep="\t"));
267
268 #=============================================================================================================
269 # Create directory structures and then set the working directory to output directory
270 #=============================================================================================================
271 if(! file.exists(outdir))
272 {
273 dir.create(outdir);
274 }
275
276 #tempdir = paste(outdir,"/",imagesubdirprefix,sep="",collapse="");
277 #if(! file.exists(tempdir))
278 #{
279 # dir.create(tempdir);
280 #}
281
282 #tempdir = paste(outdir,"/",datasubdirprefix,sep="",collapse="");
283 #if(! file.exists(tempdir))
284 #{
285 # dir.create(tempdir);
286 #}
287
288 #setwd(outdir);
289 logfile = paste(outdir,"/", "Result.log",sep="",collapse="");
290
291 #=============================================================================================================
292 # Write initial data summary in html outfile
293 #=============================================================================================================
294 cat("<html><body>\n", file = htmloutfile);
295 cat("<h1>Association between proteomics and transcriptomics data</h1>\n",
296 "<font color='blue'><h3>Input data summary</h3></font>",
297 "<ul>",
298 "<li>Abbrebiations used: PE (Proteomics) and GE (Transcriptomics)","</li>",
299 "<li>Input PE data dimension (Row Column): ", dim(PE_df),"</li>",
300 "<li>Input GE data dimension (Row Column): ", dim(GE_df),"</li>",
301 #"<li>Organism selected: ", Organism,"</li>",
302 "<li>Protein ID fetched from column: ", PE_idcolno,"</li>",
303 "<li>Transcript ID fetched from column: ", GE_idcolno,"</li>",
304 "<li>Protein ID type: ", PE_idtype,"</li>",
305 "<li>Transcript ID type: ", GE_idtype,"</li>",
306 "<li>Protein expression data fetched from column: ", PE_expcolno,"</li>",
307 "<li>Transcript expression data fetched from column: ", GE_expcolno,"</li>",
308 file = htmloutfile, append = TRUE);
309
310 #=============================================================================================================
311 # Write initial data summary in logfile
312 #=============================================================================================================
313 #cat("Current work dir:", outdir,"\n");
314 cat("Processing started\n---------------------\n", file=logfile);
315 cat("Abbrebiations used: PE (Proteomics) and GE (Transcriptomics)\n", file=logfile, append=T);
316 cat("Input PE data dimension (Row Column): ", dim(PE_df),"\n", file=logfile, append=T)
317 cat("Input GE data dimension (Row Column): ", dim(GE_df),"\n", file=logfile, append=T)
318 #cat("Organism selected: ", Organism,"\n", file=logfile, append=T)
319 #cat("Biomart map file used: ", biomart_mapfilename,"\n", file=logfile, append=T)
320 cat("Ensembl Biomart mapping data dimension (Row Column): ", dim(biomart_df),"\n", file=logfile, append=T)
321 cat("\n\nProtein ID to Transcript ID mapping started\n----------------\n", file=logfile, append=T)
322 cat("Protein ID fetched from column:", PE_idcolno,"\n", file=logfile, append=T)
323 cat("Transcript ID fetched from column:", GE_idcolno, "\n",file=logfile, append=T)
324 cat("Protein ID type:", PE_idtype, "\n",file=logfile, append=T)
325 cat("Transcript ID type:", GE_idtype,"\n", file=logfile, append=T);
326 cat("Protein expression data fetched from column:", PE_expcolno,"\n", file=logfile, append=T)
327 cat("Transcript expression data fetched from column:", GE_expcolno, "\n",file=logfile, append=T)
328
329 #=============================================================================================================
330 # Mapping starts here
331 # Pseudocode
332 # Loop over each row of PE file, fetch protein_id
333 # Search the biomartmap file and obtain corresponding transcript_id
334 # Take the mapped transcript_id and search in GE file, which row it correspond
335 # Store the PE rowno and GE rowno in rowpair object
336 #=============================================================================================================
337 rowpair = data.frame(PE_rowno = 0, GE_rowno = 0);
338 cat("Total rows:", nrow(PE_df),"\n");
339 cat("\n\nTotal protein ids to be mapped: ", nrow(PE_df),"\n", file=logfile, append=T);
340 messagelog = "\n\nBelow are protein IDs, for which no match is observed in Ensembl Biomart Map file.\n\n";
341
342 # GE_id column
343 GE_ids = GE_df[,GE_idcolno];
344 GE_ids = gsub(x=GE_ids, pattern=".\\d+$", replacement=""); # Remove version
345
346 # Loop over every row of PE data (PE_df)
347 for(i in 1:nrow(PE_df))
348 {
349
350 if(i%%100 ==0)
351 {
352 cat("Total rows processed ", i,"\n", file=logfile, append=T);
353 print(i);
354 }
355
356 queryid = PE_df[i,PE_idcolno];
357 #queryid = gsub(x=queryid, pattern=".\\d+$", replacement=""); # Remove version
358 #print(queryid);
359
360 PE_df_matchrowno = i; # Row number in PE_df which matches queryid
361
362 if(PE_idtype == "Uniprot")
363 {
364 biomart_matchrowno = which(biomart_df[,8] == queryid | biomart_df[,9] == queryid); # Row number of biomart_df which matches queryid
365 }else{
366 biomart_matchrowno = which(biomart_df[,PE_idtype] == queryid); # Row number of biomart_df which matches queryid
367 }
368
369 # If match found, map protein id to GE id and find corresponding match row number of GE_df
370 if(length(biomart_matchrowno)>0)
371 {
372 GE_df_matchrowno = which(GE_ids %in% biomart_df[biomart_matchrowno[1],GE_idtype]);
373 rowpair = rbind( rowpair, c(PE_df_matchrowno, GE_df_matchrowno));
374 if(length(GE_df_matchrowno) > 1)
375 {
376 cat("\nFor protein ID ", i," multiple transcript mapping found\n", file=logfile, append=T);
377
378 cat(
379 "<br><font color=",'red',">For protein ID", i," multiple transcript mapping found</font><br>",file = htmloutfile, append = TRUE);
380 }
381 }else{
382 messagelog = paste(messagelog, queryid, "\n",sep="", collapse="");
383 }
384 }
385 rowpair = rowpair[-1,];
386
387 #=============================================================================================================
388 # Write mapping summary, mapped and unmapped data
389 #=============================================================================================================
390 cat(
391 "<li>Total Protein ID mapped: ", length(intersect(1:nrow(PE_df), rowpair[,1])),"</li>",
392 "<li>Total Protein ID unmapped: ", length(setdiff(1:nrow(PE_df), rowpair[,1])),"</li>",
393 "<li>Total Transcript ID mapped: ", length(intersect(1:nrow(GE_df), rowpair[,2])),"</li>",
394 "<li>Total Transcript ID unmapped: ", length(setdiff(1:nrow(GE_df), rowpair[,2])),"</li></ul>",
395 file = htmloutfile, append = TRUE);
396
397 cat("\n\nMapping Statistics\n---------------------\n", file=logfile, append=T);
398 cat("Total Protein ID mapped:", length(intersect(1:nrow(PE_df), rowpair[,1])), "\n", file=logfile, append=T)
399 cat("Total Protein ID unmapped:", length(setdiff(1:nrow(PE_df), rowpair[,1])), "\n", file=logfile, append=T)
400 cat("Total Transcript ID mapped:", length(intersect(1:nrow(GE_df), rowpair[,2])), "\n", file=logfile, append=T)
401 cat("Total Transcript ID unmapped:", length(setdiff(1:nrow(GE_df), rowpair[,2])), "\n", file=logfile, append=T)
402
403 cat(messagelog,"\n",file=logfile, append=T);
404
405 if(writeMapUnmap)
406 {
407 write.table(PE_df[rowpair[,1], ], file=paste(outdir,"/",PE_outfile_mapped,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
408 write.table(GE_df[rowpair[,2], ], file= paste(outdir,"/",GE_outfile_mapped,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
409 write.table(PE_df[-rowpair[,1], ], file= paste(outdir,"/",PE_outfile_unmapped,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
410 write.table(GE_df[-rowpair[,2], ], file=paste(outdir,"/",GE_outfile_unmapped,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n");
411
412 cat(
413 "<font color='blue'><h3>Download mapped unmapped data</h3></font>",
414 "<ul><li>Protein mapped data: ", '<a href="',paste(outdir,"/",PE_outfile_mapped,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
415 "<li>Protein unmapped data: ", '<a href="',paste(outdir,"/",PE_outfile_unmapped,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
416 "<li>Transcript mapped data: ", '<a href="',paste(outdir,"/",GE_outfile_mapped,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
417 "<li>Transcript unmapped data: ", '<a href="',paste(outdir,"/",GE_outfile_unmapped,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
418 file = htmloutfile, append = TRUE);
419 }
420
421 write.table(PE_df[rowpair[,1], c(PE_idcolno,PE_expcolno)], file=paste(outdir,"/",PE_expfile,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
422 write.table(GE_df[rowpair[,2], c(GE_idcolno,GE_expcolno)], file=paste(outdir,"/",GE_expfile,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
423
424 cat(
425 "<li>Protein abundance data: ", '<a href="',paste(outdir,"/",PE_expfile,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
426 "<li>Transcript abundance data: ", '<a href="',paste(outdir,"/",GE_expfile,sep="",collapse=""),'" target="_blank"> Link</a>',"</li></ul>",
427 file = htmloutfile, append = TRUE);
428
429 #==========================================================================================
430 # Analysis (correlation and regression) starts here
431 #==========================================================================================
432 cat("Analysis started\n---------------------------\n",file=logfile, append=T);
433 proteome_df = PE_df[rowpair[,1], c(PE_idcolno,PE_expcolno)];
434 transcriptome_df = GE_df[rowpair[,2], c(GE_idcolno,GE_expcolno)];
435 nPE = nrow(proteome_df);
436 nGE = nrow(transcriptome_df)
437
438 cat("Total Protein ID: ",nPE,"\n",file=logfile, append=T);
439 cat("Total Transcript ID: ",nGE,"\n",file=logfile, append=T);
440
441 #==========================================================================================
442 # Data summary
443 #==========================================================================================
444 cat(
445 "<ul>",
446 "<li>Number of entries in Transcriptome data used for correlation: ",nPE,"</li>",
447 "<li>Number of entries in Proteome data used for correlation: ",nGE,"</li></ul>",
448 file = htmloutfile, append = TRUE);
449
450 #=============================================================================================================
451 # Remove entries with NA or Inf or -Inf in Transcriptome and Proteome data which will create problem in correlation analysis
452 #=============================================================================================================
453 totna = sum(is.na(transcriptome_df[,2]) | is.na(proteome_df[,2]));
454 totinf = sum(is.infinite(transcriptome_df[,2]) | is.infinite(proteome_df[,2]));
455
456 cat("<font color='blue'><h3>Filtering</h3></font>","Checking for NA or Inf or -Inf in either Transcriptome or Proteome data, if found, remove those entry<br>","<ul>","<li>Number of NA found: ",totna,"</li>","<li>Number of Inf or -Inf found: ",totinf,"</li></ul>",file = htmloutfile, append = TRUE);
457
458 cat("Total NA observed in either Transcriptome or Proteome data: ",totna,"\n",file=logfile, append=T);
459 cat("Total Inf or -Inf observed in either Transcriptome or Proteome data: ",totinf,"\n",file=logfile, append=T);
460
461 if(totna > 0 | totinf > 0)
462 {
463 excludeIndices_PE = which(is.na(proteome_df[,2]) | is.infinite(proteome_df[,2]));
464 excludeIndices_GE = which(is.na(transcriptome_df[,2]) | is.infinite(transcriptome_df[,2]));
465 excludeIndices = which(is.na(transcriptome_df[,2]) | is.infinite(transcriptome_df[,2]) | is.na(proteome_df[,2]) | is.infinite(proteome_df[,2]));
466
467 # Write excluded transcriptomics and proteomics data to file
468 write.table(proteome_df[excludeIndices_PE,], file=paste(outdir,"/",PE_outfile_excluded_naInf,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
469 write.table(transcriptome_df[excludeIndices_GE,], file=paste(outdir,"/",GE_outfile_excluded_naInf,sep="",collapse=""), row.names=F, quote=F, sep="\t", eol="\n")
470
471 # Write excluded transcriptomics and proteomics data link to html file
472 cat(
473 "<ul><li>Protein excluded data with NA or Inf or -Inf: ", '<a href="',paste(outdir,"/",PE_outfile_excluded_naInf,sep="",collapse=""),'" target="_blank"> Link</a>',"</li>",
474 "<li>Transcript excluded data with NA or Inf or -Inf: ", '<a href="',paste(outdir,"/",GE_outfile_excluded_naInf,sep="",collapse=""),'" target="_blank"> Link</a>',"</li></ul>",
475 file = htmloutfile, append = TRUE);
476
477 # Keep the unexcluded entries only
478 transcriptome_df = transcriptome_df[-excludeIndices,];
479 proteome_df = proteome_df[-excludeIndices,];
480 nPE = nrow(proteome_df);
481 nGE = nrow(transcriptome_df)
482
483 cat("<font color='blue'><h3>Filtered data summary</h3></font>",
484 "Excluding entires with abundance values: NA/Inf/-Inf<br>",
485 "<ul>",
486 "<li>Number of entries in Transcriptome data remained: ",nrow(transcriptome_df),"</li>",
487 "<li>Number of entries in Proteome data remained: ",nrow(proteome_df),"</li></ul>", file = htmloutfile, append = TRUE);
488
489 cat("Excluding entires with abundance values: NA/Inf/-Inf","\n",file=logfile, append=T);
490
491 cat("Total Protein ID after filtering: ",nPE,"\n",file=logfile, append=T);
492 cat("Total Transcript ID after filtering: ",nGE,"\n",file=logfile, append=T);
493
494 }
495
496 #==========================================================================================
497 # Scaling of data
498 #==========================================================================================
499 if(doscale)
500 {
501 proteome_df[,2] = scale(proteome_df[,2], center = TRUE, scale = TRUE);
502 transcriptome_df[,2] = scale(transcriptome_df[,2], center = TRUE, scale = TRUE);
503 }
504
505 #=============================================================================================================
506 # Proteome and Transcriptome data summary
507 #=============================================================================================================
508 cat("Calculating summary of PE and GE data\n",file=logfile, append=T);
509 s1 = summary(proteome_df[,2]);
510 s2 = summary(transcriptome_df[,2])
511
512 cat(
513 "<font color='blue'><h3>Proteome data summary</h3></font>\n",
514 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Value</th></tr>',
515 file = htmloutfile, append = TRUE);
516
517 for(i in 1:length(s1))
518 {
519 cat("<tr><td>",names(s1[i]),"</td><td>", s1[i],"</td></tr>\n", file = htmloutfile, append = TRUE)
520 }
521 cat("</table>\n", file = htmloutfile, append = TRUE)
522
523 cat(
524 "<font color='blue'><h3>Transcriptome data summary</h3></font>\n",
525 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Value</th></tr>',
526 file = htmloutfile, append = TRUE);
527
528 for(i in 1:length(s2))
529 {
530 cat("<tr><td>",names(s2[i]),"</td><td>", s2[i],"</td></tr>\n", file = htmloutfile, append = TRUE)
531 }
532 cat("</table>\n", file = htmloutfile, append = TRUE)
533
534 #=============================================================================================================
535 # Distribution of proteome and transcriptome abundance (Box and Density plot)
536 #=============================================================================================================
537 cat("Generating Box and Density plot\n",file=logfile, append=T);
538 outplot = paste("./",outdir,"/AbundancePlot.png",sep="",collapse="");
539 png(outplot);
540 par(mfrow=c(2,2));
541 boxplot(proteome_df[,2], ylab="Abundance", main="Proteome abundance", cex.lab=1.5);
542 plot(density(proteome_df[,2]), xlab="Protein Abundance", ylab="Density", main="Proteome abundance", cex.lab=1.5);
543 boxplot(transcriptome_df[,2], ylab="Abundance", main="Transcriptome abundance", cex.lab=1.5);
544 plot(density(transcriptome_df[,2]), xlab="Transcript Abundance", ylab="Density", main="Transcriptome abundance", cex.lab=1.5);
545 dev.off();
546
547 cat(
548 "<font color='blue'><h3>Distribution of Proteome and Transcripome abundance (Box plot and Density plot)</h3></font>\n",
549 '<img src="AbundancePlot.png">',
550 file = htmloutfile, append = TRUE);
551
552 #=============================================================================================================
553 # Scatter plot
554 #=============================================================================================================
555 cat("Generating scatter plot\n",file=logfile, append=T);
556 outplot = paste("./",outdir,"/AbundancePlot_scatter.png",sep="",collapse="");
557 png(outplot);
558 par(mfrow=c(1,1));
559 scatter.smooth(transcriptome_df[,2], proteome_df[,2], xlab="Transcript Abundance", ylab="Protein Abundance", cex.lab=1.5);
560
561 cat(
562 "<font color='blue'><h3>Scatter plot between Proteome and Transcriptome Abundance</h3></font>\n",
563 '<img src="AbundancePlot_scatter.png">',
564 file = htmloutfile, append = TRUE);
565
566 #=============================================================================================================
567 # Correlation testing
568 #=============================================================================================================
569 cat("Estimating correlation\n",file=logfile, append=T);
570 cor_result_pearson = cor.test(transcriptome_df[,2], proteome_df[,2], method = "pearson");
571 cor_result_spearman = cor.test(transcriptome_df[,2], proteome_df[,2], method = "spearman");
572 cor_result_kendall = cor.test(transcriptome_df[,2], proteome_df[,2], method = "kendall");
573
574 cat(
575 "<font color='blue'><h3>Correlation with all data</h3></font>\n",
576 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Method 1</th><th>Method 2</th><th>Method 3</th></tr>',
577 file = htmloutfile, append = TRUE);
578
579 cat(
580 "<tr><td>Correlation method used</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>",
581 "<tr><td>Correlation</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>",
582 "<tr><td>Pvalue</td><td>",cor_result_pearson$p.value,"</td><td>",cor_result_spearman$p.value,"</td><td>",cor_result_kendall$p.value,"</td></tr>",
583 file = htmloutfile, append = TRUE)
584 cat("</table>\n", file = htmloutfile, append = TRUE)
585
586 cat(
587 '<font color="red">*Note that <u>correlation</u> is <u>sensitive to outliers</u> in the data. So it is important to analyze outliers/influential observations in the data.<br> Below we use <u>cook\'s distance based approach</u> to identify such influential observations.</font>',
588 file = htmloutfile, append = TRUE);
589
590 #=============================================================================================================
591 # Linear Regression
592 #=============================================================================================================
593 cat("Fitting linear regression model\n",file=logfile, append=T);
594 PE_GE_data = proteome_df;
595 PE_GE_data = cbind(PE_GE_data, transcriptome_df);
596 colnames(PE_GE_data) = c("PE_ID","PE_abundance","GE_ID","GE_abundance");
597
598 regmodel = lm(PE_abundance~GE_abundance, data=PE_GE_data);
599 regmodel_summary = summary(regmodel);
600
601 cat(
602 "<font color='blue'><h3>Linear Regression model fit between Proteome and Transcriptome data</h3></font>\n",
603 "<p>Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.</p>\n",
604 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Value</th></tr>',
605 file = htmloutfile, append = TRUE);
606
607 cat(
608 "<tr><td>Formula</td><td>","PE_abundance~GE_abundance","</td></tr>\n",
609 "<tr><td colspan='2' align='center'> <b>Coefficients</b></td>","</tr>\n",
610 "<tr><td>",names(regmodel$coefficients[1]),"</td><td>",regmodel$coefficients[1]," (Pvalue:", regmodel_summary$coefficients[1,4],")","</td></tr>\n",
611 "<tr><td>",names(regmodel$coefficients[2]),"</td><td>",regmodel$coefficients[2]," (Pvalue:", regmodel_summary$coefficients[2,4],")","</td></tr>\n",
612 "<tr><td colspan='2' align='center'> <b>Model parameters</b></td>","</tr>\n",
613 "<tr><td>Residual standard error</td><td>",regmodel_summary$sigma," (",regmodel_summary$df[2]," degree of freedom)</td></tr>\n",
614 "<tr><td>F-statistic</td><td>",regmodel_summary$fstatistic[1]," ( on ",regmodel_summary$fstatistic[2]," and ",regmodel_summary$fstatistic[3]," degree of freedom)</td></tr>\n",
615 "<tr><td>R-squared</td><td>",regmodel_summary$r.squared,"</td></tr>\n",
616 "<tr><td>Adjusted R-squared</td><td>",regmodel_summary$adj.r.squared,"</td></tr>\n",
617 file = htmloutfile, append = TRUE)
618 cat("</table>\n", file = htmloutfile, append = TRUE)
619
620 #=============================================================================================================
621 # Plotting various regression diagnostics plots
622 #=============================================================================================================
623 outplot1 = paste("./",outdir,"/PE_GE_modelfit.pdf",sep="",collapse="");
624 pdf(outplot1);
625 devnum = which(unlist(sapply(2:length(.Devices), function(x){attributes(.Devices[[x]])$filepath==outplot1})))+1
626 print(.Devices)
627 print(c(devnum,"+++"));
628
629 regmodel_predictedy = predict(regmodel, PE_GE_data);
630 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Linear regression with all data");
631 points(PE_GE_data[,"GE_abundance"], regmodel_predictedy, col="red");
632
633 cat("Generating regression diagnostics plot\n",file=logfile, append=T);
634 cat(
635 "<font color='blue'><h3>Plotting various regression diagnostics plots</h3></font>\n",
636 file = htmloutfile, append = TRUE);
637
638 outplot = paste("./",outdir,"/PE_GE_lm_1.png",sep="",collapse="");
639 png(outplot);
640 par(mfrow=c(1,1));
641 plot(regmodel, 1, cex.lab=1.5);
642 dev.off();
643
644 outplot = paste("./",outdir,"/PE_GE_lm_2.png",sep="",collapse="");
645 png(outplot);
646 par(mfrow=c(1,1));
647 plot(regmodel, 2, cex.lab=1.5);
648 dev.off();
649
650 outplot = paste("./",outdir,"/PE_GE_lm_3.png",sep="",collapse="");
651 png(outplot);
652 par(mfrow=c(1,1));
653 plot(regmodel, 3, cex.lab=1.5);
654 dev.off();
655
656 outplot = paste("./",outdir,"/PE_GE_lm_5.png",sep="",collapse="");
657 png(outplot);
658 par(mfrow=c(1,1));
659 plot(regmodel, 5, cex.lab=1.5);
660 dev.off();
661
662 outplot = paste("./",outdir,"/PE_GE_lm.pdf",sep="",collapse="");
663 pdf(outplot);
664 plot(regmodel);
665 dev.off();
666 regmodel_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel$fitted.values)
667
668
669 cat(
670 "<u><font color='brown'><h4>Residuals vs Fitted plot</h4></font></u>\n",
671 '<img src="PE_GE_lm_1.png">',
672 '<br><br>This plot checks for linear relationship assumptions. If a horizontal line is observed without any distinct patterns, it indicates a linear relationship<br>',
673 file = htmloutfile, append = TRUE);
674
675 cat(
676 "<u><font color='brown'><h4>Normal Q-Q plot of residuals</h4></font></u>\n",
677 '<img src="PE_GE_lm_2.png">',
678 '<br><br>This plot checks whether residuals are normally distributed or not. It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.<br>',
679 file = htmloutfile, append = TRUE);
680
681 cat(
682 "<u><font color='brown'><h4>Scale-Location (or Spread-Location) plot</h4></font></u>\n",
683 '<img src="PE_GE_lm_3.png">',
684 '<br><br>This plot checks for homogeneity of residual variance (homoscedasticity). A horizontal line observed with equally spread residual points is a good indication of homoscedasticity.<br>',
685 file = htmloutfile, append = TRUE);
686
687 cat(
688 "<u><font color='brown'><h4>Residuals vs Leverage plot</h4></font></u>\n",
689 '<img src="PE_GE_lm_3.png">',
690 '<br><br>This plot is useful to identify any influential cases, that is outliers or extreme values that might influence the regression results upon inclusion or exclusion from the analysis.<br>',
691 file = htmloutfile, append = TRUE);
692
693 #=============================================================================================================
694 # Identification of influential observations
695 #=============================================================================================================
696 cat("Identifying influential observations\n",file=logfile, append=T);
697 cat(
698 "<font color='blue'><h3>Identify influential observations</h3></font>\n",
699 file = htmloutfile, append = TRUE);
700 cat(
701 '<p><b>Cook’s distance</b> computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.<br>In general use, those observations that have a <b>cook’s distance > than 4 times the mean</b> may be classified as <b>influential.</b></p>',
702 file = htmloutfile, append = TRUE);
703
704 cooksd <- cooks.distance(regmodel);
705
706 cat("Generating cooksd plot\n",file=logfile, append=T);
707 outplot = paste("./",outdir,"/PE_GE_lm_cooksd.png",sep="",collapse="");
708 png(outplot);
709 par(mfrow=c(1,1));
710 plot(cooksd, pch="*", cex=2, cex.lab=1.5,main="Influential Obs. by Cooks distance", ylab="Cook\'s distance", xlab="Observations") # plot cooks distance
711 abline(h = 4*mean(cooksd, na.rm=T), col="red") # add cutoff line
712 #text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),""), col="red", pos=2) # add labels
713 dev.off();
714
715 cat(
716 '<img src="PE_GE_lm_cooksd.png">',
717 '<br>In the above plot, observations above red line (4*mean cook\'s distance) are influential, marked in <b>*</b>. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients<br><br>',
718 file = htmloutfile, append = TRUE);
719
720 tempind = which(cooksd>4*mean(cooksd, na.rm=T));
721 PE_GE_data_no_outlier = PE_GE_data[-tempind,];
722 PE_GE_data_no_outlier$cooksd = cooksd[-tempind]
723 PE_GE_data_outlier = PE_GE_data[tempind,];
724 PE_GE_data_outlier$cooksd = cooksd[tempind]
725
726 cat(
727 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Value</th></tr>',
728 file = htmloutfile, append = TRUE);
729
730 cat(
731 "<tr><td>Mean cook\'s distance</td><td>",mean(cooksd, na.rm=T),"</td></tr>\n",
732 "<tr><td>Total influential observations (cook\'s distance > 4 * mean cook\'s distance)</td><td>",length(tempind),"</td></tr>\n",
733 "<tr><td>Total influential observations (cook\'s distance > 3 * mean cook\'s distance)</td><td>",length(which(cooksd>3*mean(cooksd, na.rm=T))),"</td></tr>\n",
734 "</table>",
735 '<font color="brown"><h4>Top 10 influential observations (cook\'s distance > 4 * mean cook\'s distance)</h4></font>',
736 file = htmloutfile, append = TRUE);
737
738 cat("Writing influential observations\n",file=logfile, append=T);
739
740 outdatafile = paste("./",outdir,"/PE_GE_influential_observation.tsv", sep="", collapse="");
741 cat('<a href="',outdatafile, '" target="_blank">Download entire list</a>',file = htmloutfile, append = TRUE);
742 write.table(PE_GE_data_outlier, file=outdatafile, row.names=F, sep="\t", quote=F);
743
744 cat(
745 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>PE_ID</th><th>PE_abundance</th><th>GE_ID</th><th>GE_abundance</th><th>cooksd</th></tr>',
746 file = htmloutfile, append = TRUE);
747
748 for(i in 1:10)
749 {
750 cat(
751 '<tr>','<td>',PE_GE_data_outlier[i,1],'</td>',
752 '<td>',PE_GE_data_outlier[i,2],'</td>',
753 '<td>',PE_GE_data_outlier[i,3],'</td>',
754 '<td>',PE_GE_data_outlier[i,4],'</td>',
755 '<td>',PE_GE_data_outlier[i,5],'</td></tr>',
756 file = htmloutfile, append = TRUE);
757 }
758 cat('</table>',file = htmloutfile, append = TRUE);
759
760 #=============================================================================================================
761 # Correlation with removal of outliers
762 #=============================================================================================================
763
764 #=============================================================================================================
765 # Scatter plot
766 #=============================================================================================================
767 outplot = paste("./",outdir,"/AbundancePlot_scatter_without_outliers.png",sep="",collapse="");
768 png(outplot);
769 par(mfrow=c(1,1));
770 scatter.smooth(PE_GE_data_no_outlier[,"GE_abundance"], PE_GE_data_no_outlier[,"PE_abundance"], xlab="Transcript Abundance", ylab="Protein Abundance", cex.lab=1.5);
771
772 cat(
773 "<font color='blue'><h3>Scatter plot between Proteome and Transcriptome Abundance, after removal of outliers/influential observations</h3></font>\n",
774 '<img src="AbundancePlot_scatter_without_outliers.png">',
775 file = htmloutfile, append = TRUE);
776
777 #=============================================================================================================
778 # Correlation
779 #=============================================================================================================
780 cat("Estimating orrelation with removal of outliers \n",file=logfile, append=T);
781 cat(
782 "<font color='blue'><h3>Correlation with removal of outliers / influential observations</h3></font>\n",
783 '<p>We removed the influential observations and reestimated the correlation values.</p>',
784 file = htmloutfile, append = TRUE);
785
786 cor_result_pearson = cor.test(PE_GE_data_no_outlier[,"GE_abundance"], PE_GE_data_no_outlier[,"PE_abundance"], method = "pearson");
787 cor_result_spearman = cor.test(PE_GE_data_no_outlier[,"GE_abundance"], PE_GE_data_no_outlier[,"PE_abundance"], method = "spearman");
788 cor_result_kendall = cor.test(PE_GE_data_no_outlier[,"GE_abundance"], PE_GE_data_no_outlier[,"PE_abundance"], method = "kendall");
789
790 cat(
791 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Parameter</th><th>Method 1</th><th>Method 2</th><th>Method 3</th></tr>',
792 file = htmloutfile, append = TRUE);
793
794 cat(
795 "<tr><td>Correlation method used</td><td>",cor_result_pearson$method,"</td><td>",cor_result_spearman$method,"</td><td>",cor_result_kendall$method,"</td></tr>",
796 "<tr><td>Correlation</td><td>",cor_result_pearson$estimate,"</td><td>",cor_result_spearman$estimate,"</td><td>",cor_result_kendall$estimate,"</td></tr>",
797 "<tr><td>Pvalue</td><td>",cor_result_pearson$p.value,"</td><td>",cor_result_spearman$p.value,"</td><td>",cor_result_kendall$p.value,"</td></tr>",
798 file = htmloutfile, append = TRUE)
799 cat("</table>\n", file = htmloutfile, append = TRUE)
800
801 #=============================================================================================================
802 # Heatmap
803 #=============================================================================================================
804 cat(
805 "<font color='blue'><h3>Heatmap of PE and GE abundance values</h3></font>\n",
806 file = htmloutfile, append = TRUE);
807
808 cat("Generating heatmap plot\n",file=logfile, append=T);
809 outplot = paste("./",outdir,"/PE_GE_heatmap.png",sep="",collapse="");
810 png(outplot);
811 par(mfrow=c(1,1));
812 heatmap.2(as.matrix(PE_GE_data[,c("PE_abundance","GE_abundance")]), trace="none", cexCol=1, col=greenred(100),Colv=F, labCol=c("PE","GE"), scale="col");
813 dev.off();
814
815 cat(
816 '<img src="PE_GE_heatmap.png">',
817 file = htmloutfile, append = TRUE);
818
819 #=============================================================================================================
820 # kmeans clustering
821 #=============================================================================================================
822
823 PE_GE_data_kdata = PE_GE_data;
824
825
826 k1 = kmeans(PE_GE_data_kdata[,c("PE_abundance","GE_abundance")], 5);
827 cat("Generating kmeans plot\n",file=logfile, append=T);
828 outplot = paste("./",outdir,"/PE_GE_kmeans.png",sep="",collapse="");
829 png(outplot);
830 par(mfrow=c(1,1));
831 scatter.smooth(PE_GE_data_kdata[,"GE_abundance"], PE_GE_data_kdata[,"PE_abundance"], xlab="Transcript Abundance", ylab="Protein Abundance", cex.lab=1.5);
832
833 ind=which(k1$cluster==1);
834 points(PE_GE_data_kdata[ind,"GE_abundance"], PE_GE_data_kdata[ind,"PE_abundance"], col="red", pch=16);
835 ind=which(k1$cluster==2);
836 points(PE_GE_data_kdata[ind,"GE_abundance"], PE_GE_data_kdata[ind,"PE_abundance"], col="green", pch=16);
837 ind=which(k1$cluster==3);
838 points(PE_GE_data_kdata[ind,"GE_abundance"], PE_GE_data_kdata[ind,"PE_abundance"], col="blue", pch=16);
839 ind=which(k1$cluster==4);
840 points(PE_GE_data_kdata[ind,"GE_abundance"], PE_GE_data_kdata[ind,"PE_abundance"], col="cyan", pch=16);
841 ind=which(k1$cluster==5);
842 points(PE_GE_data_kdata[ind,"GE_abundance"], PE_GE_data_kdata[ind,"PE_abundance"], col="black", pch=16);
843 dev.off();
844
845 cat(
846 "<font color='blue'><h3>Kmean clustering</h3></font>\nNumber of Clusters: 5<br>",
847 file = htmloutfile, append = TRUE);
848
849 tempind = order(k1$cluster);
850 tempoutfile = paste(outdir,"/","PE_GE_kmeans_clusterpoints.txt",sep="",collapse="");
851
852 write.table(data.frame(PE_GE_data_kdata[tempind, ], Cluster=k1$cluster[tempind]), file=tempoutfile, row.names=F, quote=F, sep="\t", eol="\n")
853
854 cat('<a href="',tempoutfile, '" target="_blank">Download cluster list</a><br>',file = htmloutfile, append = TRUE);
855
856 cat(
857 '<img src="PE_GE_kmeans.png">',
858 file = htmloutfile, append = TRUE);
859
860 #=============================================================================================================
861 # Other Regression fit
862 #=============================================================================================================
863 dev.set(devnum);
864 # Linear regression with removal of outliers
865 regmodel_no_outlier = lm(PE_abundance~GE_abundance, data=PE_GE_data_no_outlier);
866 regmodel_no_outlier_predictedy = predict(regmodel_no_outlier, PE_GE_data_no_outlier);
867 outplot = paste("./",outdir,"/PE_GE_lm_without_outliers.pdf",sep="",collapse="");
868 plot(PE_GE_data_no_outlier[,"GE_abundance"], PE_GE_data_no_outlier[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Linear regression with removal of outliers");
869 points(PE_GE_data_no_outlier[,"GE_abundance"], regmodel_no_outlier_predictedy, col="red");
870
871 pdf(outplot);
872 plot(regmodel_no_outlier);
873 dev.off();
874 regmodel_no_outlier_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_no_outlier$fitted.values)
875
876 # Resistant regression (lqs / least trimmed squares method)
877 regmodel_lqs = lqs(PE_abundance~GE_abundance, data=PE_GE_data);
878 regmodel_lqs_predictedy = predict(regmodel_lqs, PE_GE_data);
879 outplot = paste("./",outdir,"/PE_GE_lqs.pdf",sep="",collapse="");
880 pdf(outplot);
881 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Resistant regression (lqs / least trimmed squares method)");
882 points(PE_GE_data[,"GE_abundance"], regmodel_lqs_predictedy, col="red");
883 #plot(regmodel_lqs);
884 dev.off();
885 dev.set(devnum);
886 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Resistant regression (lqs / least trimmed squares method)");
887 points(PE_GE_data[,"GE_abundance"], regmodel_lqs_predictedy, col="red");
888 regmodel_lqs_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_lqs$fitted.values)
889
890 # Robust regression (rlm / Huber M-estimator method)
891 regmodel_rlm = rlm(PE_abundance~GE_abundance, data=PE_GE_data);
892 regmodel_rlm_predictedy = predict(regmodel_rlm, PE_GE_data);
893 outplot = paste("./",outdir,"/PE_GE_rlm.pdf",sep="",collapse="");
894 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Robust regression (rlm / Huber M-estimator method)");
895 points(PE_GE_data[,"GE_abundance"], regmodel_rlm_predictedy, col="red");
896 pdf(outplot);
897 plot(regmodel_rlm);
898 dev.off();
899 regmodel_rlm_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_rlm$fitted.values)
900
901 # polynomical reg
902 regmodel_poly2 = lm(PE_abundance ~ poly(GE_abundance, 2, raw = TRUE), data = PE_GE_data)
903 regmodel_poly3 = lm(PE_abundance ~ poly(GE_abundance, 3, raw = TRUE), data = PE_GE_data)
904 regmodel_poly4 = lm(PE_abundance ~ poly(GE_abundance, 4, raw = TRUE), data = PE_GE_data)
905 regmodel_poly5 = lm(PE_abundance ~ poly(GE_abundance, 5, raw = TRUE), data = PE_GE_data)
906 regmodel_poly6 = lm(PE_abundance ~ poly(GE_abundance, 6, raw = TRUE), data = PE_GE_data)
907 regmodel_poly2_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_poly2$fitted.values)
908 regmodel_poly3_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_poly3$fitted.values)
909 regmodel_poly4_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_poly4$fitted.values)
910 regmodel_poly5_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_poly5$fitted.values)
911 regmodel_poly6_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_poly6$fitted.values)
912 regmodel_poly2_predictedy = predict(regmodel_poly2, PE_GE_data);
913 regmodel_poly3_predictedy = predict(regmodel_poly3, PE_GE_data);
914 regmodel_poly4_predictedy = predict(regmodel_poly4, PE_GE_data);
915 regmodel_poly5_predictedy = predict(regmodel_poly5, PE_GE_data);
916 regmodel_poly6_predictedy = predict(regmodel_poly6, PE_GE_data);
917
918 outplot = paste("./",outdir,"/PE_GE_poly2.pdf",sep="",collapse="");
919 dev.set(devnum);
920 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Polynomial regression with degree 2");
921 points(PE_GE_data[,"GE_abundance"], regmodel_poly2_predictedy, col="red");
922 pdf(outplot);
923 plot(regmodel_poly2);
924 dev.off();
925
926 outplot = paste("./",outdir,"/PE_GE_poly3.pdf",sep="",collapse="");
927 dev.set(devnum);
928 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Polynomial regression with degree 3");
929 points(PE_GE_data[,"GE_abundance"], regmodel_poly3_predictedy, col="red");
930 pdf(outplot);
931 plot(regmodel_poly3);
932 dev.off();
933
934 outplot = paste("./",outdir,"/PE_GE_poly4.pdf",sep="",collapse="");
935 dev.set(devnum);
936 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Polynomial regression with degree 4");
937 points(PE_GE_data[,"GE_abundance"], regmodel_poly4_predictedy, col="red");
938 pdf(outplot);
939 plot(regmodel_poly4);
940 dev.off();
941
942 outplot = paste("./",outdir,"/PE_GE_poly5.pdf",sep="",collapse="");
943 dev.set(devnum);
944 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Polynomial regression with degree 5");
945 points(PE_GE_data[,"GE_abundance"], regmodel_poly5_predictedy, col="red");
946 pdf(outplot);
947 plot(regmodel_poly5);
948 dev.off();
949
950 outplot = paste("./",outdir,"/PE_GE_poly6.pdf",sep="",collapse="");
951 dev.set(devnum);
952 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Polynomial regression with degree 6");
953 points(PE_GE_data[,"GE_abundance"], regmodel_poly6_predictedy, col="red");
954 pdf(outplot);
955 plot(regmodel_poly6);
956 dev.off();
957
958 # GAM Generalized additive models
959 regmodel_gam <- gam(PE_abundance ~ s(GE_abundance), data = PE_GE_data)
960 regmodel_gam_predictedy = predict(regmodel_gam, PE_GE_data);
961
962 regmodel_gam_metrics = regr.eval(PE_GE_data$PE_abundance, regmodel_gam$fitted.values)
963 outplot = paste("./",outdir,"/PE_GE_gam.pdf",sep="",collapse="");
964 dev.set(devnum);
965 plot(PE_GE_data[,"GE_abundance"], PE_GE_data[,"PE_abundance"], xlab="GE_abundance", ylab="PE_abundance",main="Generalized additive models");
966 points(PE_GE_data[,"GE_abundance"], regmodel_gam_predictedy, col="red");
967 pdf(outplot);
968 plot(regmodel_gam,pages=1,residuals=TRUE); ## show partial residuals
969 plot(regmodel_gam,pages=1,seWithMean=TRUE) ## `with intercept' CIs
970 dev.off();
971 dev.off(devnum);
972
973 cat(
974 "<font color='blue'><h3>Other regression model fitting</h3></font>\n",
975 file = htmloutfile, append = TRUE);
976
977 cat(
978 "<ul>
979 <li>MAE:mean absolute error</li>
980 <li>MSE: mean squared error</li>
981 <li>RMSE:root mean squared error ( sqrt(MSE) )</li>
982 <li>MAPE:mean absolute percentage error</li>
983 </ul>
984 ",
985 file = htmloutfile, append = TRUE);
986
987 cat(
988 '<h4><a href="PE_GE_modelfit.pdf" target="_blank">Comparison of model fits</a></h4>',
989 file = htmloutfile, append = TRUE);
990
991 cat(
992 '<table class="embedded-table" border=1 cellspacing=0 cellpadding=5 style="table-layout:auto; "> <tr bgcolor="#c3f0d6"><th>Model</th><th>MAE</th><th>MSE</th><th>RMSE</th><th>MAPE</th><th>Diagnostics Plot</th></tr>',
993 file = htmloutfile, append = TRUE);
994
995 cat(
996 "<tr><td>Linear regression with all data</td><td>",regmodel_metrics[1],"</td><td>",regmodel_metrics[2],"</td><td>",regmodel_metrics[3],"</td><td>",regmodel_metrics[4],"</td><td>",'<a href="PE_GE_lm.pdf" target="_blank">Link</a>',"</td></tr>",
997
998 "<tr><td>Linear regression with removal of outliers</td><td>",regmodel_no_outlier_metrics[1],"</td><td>",regmodel_no_outlier_metrics[2],"</td><td>",regmodel_no_outlier_metrics[3],"</td><td>",regmodel_no_outlier_metrics[4],"</td><td>",'<a href="PE_GE_lm_without_outliers.pdf" target="_blank">Link</a>',"</td></tr>",
999
1000 "<tr><td>Resistant regression (lqs / least trimmed squares method)</td><td>",regmodel_lqs_metrics[1],"</td><td>",regmodel_lqs_metrics[2],"</td><td>",regmodel_lqs_metrics[3],"</td><td>",regmodel_lqs_metrics[4],"</td><td>", '<a href="PE_GE_lqs.pdf" target="_blank">Link</a>',"</td></tr>",
1001
1002 "<tr><td>Robust regression (rlm / Huber M-estimator method)</td><td>",regmodel_rlm_metrics[1],"</td><td>",regmodel_rlm_metrics[2],"</td><td>",regmodel_rlm_metrics[3],"</td><td>",regmodel_rlm_metrics[4],"</td><td>",'<a href="PE_GE_rlm.pdf" target="_blank">Link</a>',"</td></tr>",
1003
1004
1005 "<tr><td>Polynomial regression with degree 2</td><td>",regmodel_poly2_metrics[1],"</td><td>",regmodel_poly2_metrics[2],"</td><td>",regmodel_poly2_metrics[3],"</td><td>",regmodel_poly2_metrics[4],"</td><td>",'<a href="PE_GE_poly2.pdf" target="_blank">Link</a>',"</td></tr>",
1006
1007 "<tr><td>Polynomial regression with degree 3</td><td>",regmodel_poly3_metrics[1],"</td><td>",regmodel_poly3_metrics[2],"</td><td>",regmodel_poly3_metrics[3],"</td><td>",regmodel_poly3_metrics[4],"</td><td>",'<a href="PE_GE_poly3.pdf" target="_blank">Link</a>',"</td></tr>",
1008
1009 "<tr><td>Polynomial regression with degree 4</td><td>",regmodel_poly4_metrics[1],"</td><td>",regmodel_poly4_metrics[2],"</td><td>",regmodel_poly4_metrics[3],"</td><td>",regmodel_poly4_metrics[4],"</td><td>",'<a href="PE_GE_poly4.pdf" target="_blank">Link</a>',"</td></tr>",
1010
1011 "<tr><td>Polynomial regression with degree 5</td><td>",regmodel_poly5_metrics[1],"</td><td>",regmodel_poly5_metrics[2],"</td><td>",regmodel_poly5_metrics[3],"</td><td>",regmodel_poly5_metrics[4],"</td><td>",'<a href="PE_GE_poly5.pdf" target="_blank">Link</a>',"</td></tr>",
1012
1013 "<tr><td>Polynomial regression with degree 6</td><td>",regmodel_poly6_metrics[1],"</td><td>",regmodel_poly6_metrics[2],"</td><td>",regmodel_poly6_metrics[3],"</td><td>",regmodel_poly6_metrics[4],"</td><td>",'<a href="PE_GE_poly6.pdf" target="_blank">Link</a>',"</td></tr>",
1014
1015 "<tr><td>Generalized additive models</td><td>",regmodel_gam_metrics[1],"</td><td>",regmodel_gam_metrics[2],"</td><td>",regmodel_gam_metrics[3],"</td><td>",regmodel_gam_metrics[4],"</td><td>",'<a href="PE_GE_gam.pdf" target="_blank">Link</a>',"</td></tr>",
1016
1017 "</table>",
1018 file = htmloutfile, append = TRUE);
1019
1020
1021 # Warning On
1022 options(warn = oldw)
1023
1024
1025