Mercurial > repos > shians > shrnaseq
annotate hairpinTool.R @ 7:91e411fcdecc
Version 1.0.8
- Added differential representation counts table
| author | shian_su <registertonysu@gmail.com> |
|---|---|
| date | Wed, 23 Apr 2014 14:05:26 +1000 |
| parents | 3d04308a99f9 |
| children | 548802b3492f |
| rev | line source |
|---|---|
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1 # ARGS: 1.inputType -String specifying format of input (fastq or table) |
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2 # IF inputType is "fastQ": |
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3 # 2*.fastqPath -One or more strings specifying path to fastq files |
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4 # 2.annoPath -String specifying path to hairpin annotation table |
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5 # 3.samplePath -String specifying path to sample annotation table |
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6 # 4.barStart -Integer specifying starting position of barcode |
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7 # 5.barEnd -Integer specifying ending position of barcode |
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8 # 6.hpStart -Integer specifying startins position of hairpin |
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9 # unique region |
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10 # 7.hpEnd -Integer specifying ending position of hairpin |
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11 # unique region |
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12 # ### |
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13 # IF inputType is "counts": |
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14 # 2.countPath -String specifying path to count table |
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15 # 3.annoPath -String specifying path to hairpin annotation table |
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16 # 4.samplePath -String specifying path to sample annotation table |
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17 # ### |
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18 # 8.cpmReq -Float specifying cpm requirement |
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19 # 9.sampleReq -Integer specifying cpm requirement |
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20 # 10.fdrThresh -Float specifying the FDR requirement |
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21 # 11.lfcThresh -Float specifying the log-fold-change requirement |
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22 # 12.workMode -String specifying exact test or GLM usage |
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23 # 13.htmlPath -String specifying path to HTML file |
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24 # 14.folderPath -STring specifying path to folder for output |
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25 # IF workMode is "classic" (exact test) |
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26 # 15.pairData[2] -String specifying first group for exact test |
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27 # 16.pairData[1] -String specifying second group for exact test |
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28 # ### |
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29 # IF workMode is "glm" |
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30 # 15.contrastData -String specifying contrasts to be made |
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31 # 16.roastOpt -String specifying usage of gene-wise tests |
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32 # 17.hairpinReq -String specifying hairpin requirement for gene- |
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33 # wise test |
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34 # 18.selectOpt -String specifying type of selection for barcode |
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35 # plots |
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36 # 19.selectVals -String specifying members selected for barcode |
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37 # plots |
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38 # |
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39 # OUT: Bar Plot of Counts Per Index |
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40 # Bar Plot of Counts Per Hairpin |
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41 # MDS Plot |
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42 # Smear Plot |
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43 # Barcode Plots (If Genewise testing was selected) |
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44 # Top Expression Table |
| 7 | 45 # Feature Counts Table |
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46 # HTML file linking to the ouputs |
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47 # |
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48 # Author: Shian Su - registertonysu@gmail.com - Jan 2014 |
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49 |
| 2 | 50 # Record starting time |
| 51 timeStart <- as.character(Sys.time()) | |
| 52 | |
| 53 # Loading and checking required packages | |
| 54 library(methods, quietly=TRUE, warn.conflicts=FALSE) | |
| 55 library(statmod, quietly=TRUE, warn.conflicts=FALSE) | |
| 56 library(splines, quietly=TRUE, warn.conflicts=FALSE) | |
| 57 library(edgeR, quietly=TRUE, warn.conflicts=FALSE) | |
| 58 library(limma, quietly=TRUE, warn.conflicts=FALSE) | |
| 59 | |
| 60 if (packageVersion("edgeR") < "3.5.23") { | |
| 61 message("Please update 'edgeR' to version >= 3.5.23 to run this script") | |
| 62 } | |
| 63 | |
| 64 ################################################################################ | |
| 65 ### Function declarations | |
| 66 ################################################################################ | |
| 67 | |
| 7 | 68 # Function to load libaries without messages |
| 69 silentLibrary <- function(...) { | |
| 70 list <- c(...) | |
| 71 for (package in list){ | |
| 72 suppressPackageStartupMessages(library(package, character.only=TRUE)) | |
| 73 } | |
| 74 } | |
| 75 | |
| 2 | 76 # Function to sanitise contrast equations so there are no whitespaces |
| 77 # surrounding the arithmetic operators, leading or trailing whitespace | |
| 78 sanitiseEquation <- function(equation) { | |
| 79 equation <- gsub(" *[+] *", "+", equation) | |
| 80 equation <- gsub(" *[-] *", "-", equation) | |
| 81 equation <- gsub(" *[/] *", "/", equation) | |
| 82 equation <- gsub(" *[*] *", "*", equation) | |
| 83 equation <- gsub("^\\s+|\\s+$", "", equation) | |
| 84 return(equation) | |
| 85 } | |
| 86 | |
| 87 # Function to sanitise group information | |
| 88 sanitiseGroups <- function(string) { | |
| 89 string <- gsub(" *[,] *", ",", string) | |
| 90 string <- gsub("^\\s+|\\s+$", "", string) | |
| 91 return(string) | |
| 92 } | |
| 93 | |
| 94 # Function to change periods to whitespace in a string | |
| 95 unmake.names <- function(string) { | |
| 96 string <- gsub(".", " ", string, fixed=TRUE) | |
| 97 return(string) | |
| 98 } | |
| 99 | |
| 100 # Function has string input and generates an output path string | |
| 101 makeOut <- function(filename) { | |
| 102 return(paste0(folderPath, "/", filename)) | |
| 103 } | |
| 104 | |
| 105 # Function has string input and generates both a pdf and png output strings | |
| 106 imgOut <- function(filename) { | |
| 107 assign(paste0(filename, "Png"), makeOut(paste0(filename,".png")), | |
| 7 | 108 envir=.GlobalEnv) |
| 2 | 109 assign(paste0(filename, "Pdf"), makeOut(paste0(filename,".pdf")), |
| 7 | 110 envir=.GlobalEnv) |
| 2 | 111 } |
| 112 | |
| 113 # Create cat function default path set, default seperator empty and appending | |
| 114 # true by default (Ripped straight from the cat function with altered argument | |
| 115 # defaults) | |
| 7 | 116 cata <- function(..., file=htmlPath, sep="", fill=FALSE, labels=NULL, |
| 117 append=TRUE) { | |
| 2 | 118 if (is.character(file)) |
| 119 if (file == "") | |
| 120 file <- stdout() | |
| 121 else if (substring(file, 1L, 1L) == "|") { | |
| 122 file <- pipe(substring(file, 2L), "w") | |
| 123 on.exit(close(file)) | |
| 124 } | |
| 125 else { | |
| 126 file <- file(file, ifelse(append, "a", "w")) | |
| 127 on.exit(close(file)) | |
| 128 } | |
| 129 .Internal(cat(list(...), file, sep, fill, labels, append)) | |
| 130 } | |
| 131 | |
| 132 # Function to write code for html head and title | |
| 133 HtmlHead <- function(title) { | |
| 134 cata("<head>\n") | |
| 135 cata("<title>", title, "</title>\n") | |
| 136 cata("</head>\n") | |
| 137 } | |
| 138 | |
| 139 # Function to write code for html links | |
| 140 HtmlLink <- function(address, label=address) { | |
| 141 cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n") | |
| 142 } | |
| 143 | |
| 144 # Function to write code for html images | |
| 145 HtmlImage <- function(source, label=source, height=600, width=600) { | |
| 146 cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height) | |
| 147 cata("\" width=\"", width, "\"/>\n") | |
| 148 } | |
| 149 | |
| 150 # Function to write code for html list items | |
| 151 ListItem <- function(...) { | |
| 152 cata("<li>", ..., "</li>\n") | |
| 153 } | |
| 154 | |
| 155 TableItem <- function(...) { | |
| 156 cata("<td>", ..., "</td>\n") | |
| 157 } | |
| 158 | |
| 159 TableHeadItem <- function(...) { | |
| 160 cata("<th>", ..., "</th>\n") | |
| 161 } | |
| 162 ################################################################################ | |
| 163 ### Input Processing | |
| 164 ################################################################################ | |
| 165 | |
| 166 # Grabbing arguments from command line | |
| 167 argv <- commandArgs(TRUE) | |
| 168 | |
| 169 # Remove fastq file paths after collecting from argument vector | |
| 170 inputType <- as.character(argv[1]) | |
| 171 if (inputType=="fastq") { | |
| 172 fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)], | |
| 173 fixed=TRUE)) | |
| 174 argv <- argv[!grepl("fastq::", argv, fixed=TRUE)] | |
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175 annoPath <- as.character(argv[2]) |
| 2 | 176 samplePath <- as.character(argv[3]) |
| 177 barStart <- as.numeric(argv[4]) | |
| 178 barEnd <- as.numeric(argv[5]) | |
| 179 hpStart <- as.numeric(argv[6]) | |
| 180 hpEnd <- as.numeric(argv[7]) | |
| 181 } else if (inputType=="counts") { | |
| 182 countPath <- as.character(argv[2]) | |
| 183 annoPath <- as.character(argv[3]) | |
| 184 samplePath <- as.character(argv[4]) | |
| 185 } | |
| 186 | |
| 187 cpmReq <- as.numeric(argv[8]) | |
| 188 sampleReq <- as.numeric(argv[9]) | |
| 189 fdrThresh <- as.numeric(argv[10]) | |
| 190 lfcThresh <- as.numeric(argv[11]) | |
| 191 workMode <- as.character(argv[12]) | |
| 192 htmlPath <- as.character(argv[13]) | |
| 193 folderPath <- as.character(argv[14]) | |
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194 |
| 2 | 195 if (workMode=="classic") { |
| 196 pairData <- character() | |
| 197 pairData[2] <- as.character(argv[15]) | |
| 198 pairData[1] <- as.character(argv[16]) | |
| 199 } else if (workMode=="glm") { | |
| 200 contrastData <- as.character(argv[15]) | |
| 201 roastOpt <- as.character(argv[16]) | |
| 202 hairpinReq <- as.numeric(argv[17]) | |
| 203 selectOpt <- as.character(argv[18]) | |
| 204 selectVals <- as.character(argv[19]) | |
| 205 } | |
| 206 | |
| 207 # Read in inputs | |
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208 |
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209 samples <- read.table(samplePath, header=TRUE, sep="\t") |
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210 anno <- read.table(annoPath, header=TRUE, sep="\t") |
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211 if (inputType=="counts") { |
| 2 | 212 counts <- read.table(countPath, header=TRUE, sep="\t") |
| 213 } | |
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214 |
| 2 | 215 ###################### Check inputs for correctness ############################ |
| 216 samples$ID <- make.names(samples$ID) | |
| 217 | |
| 218 if (!any(grepl("group", names(samples)))) { | |
| 219 stop("'group' column not specified in sample annotation file") | |
| 220 } # Check if grouping variable has been specified | |
| 221 | |
| 222 if (any(table(samples$ID)>1)){ | |
| 223 tab <- table(samples$ID) | |
| 224 offenders <- paste(names(tab[tab>1]), collapse=", ") | |
| 225 offenders <- unmake.names(offenders) | |
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226 stop("'ID' column of sample annotation must have unique values, values ", |
| 2 | 227 offenders, " are repeated") |
| 228 } # Check that IDs in sample annotation are unique | |
| 229 | |
| 230 if (inputType=="fastq") { | |
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231 |
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232 if (any(table(anno$ID)>1)){ |
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233 tab <- table(anno$ID) |
| 2 | 234 offenders <- paste(names(tab[tab>1]), collapse=", ") |
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235 stop("'ID' column of hairpin annotation must have unique values, values ", |
| 2 | 236 offenders, " are repeated") |
| 237 } # Check that IDs in hairpin annotation are unique | |
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238 |
| 2 | 239 } else if (inputType=="counts") { |
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240 if (any(is.na(match(samples$ID, colnames(counts))))) { |
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241 stop("not all samples have groups specified") |
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242 } # Check that a group has be specifed for each sample |
| 2 | 243 |
| 244 if (any(table(counts$ID)>1)){ | |
| 245 tab <- table(counts$ID) | |
| 246 offenders <- paste(names(tab[tab>1]), collapse=", ") | |
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247 stop("'ID' column of count table must have unique values, values ", |
| 2 | 248 offenders, " are repeated") |
| 249 } # Check that IDs in count table are unique | |
| 250 } | |
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251 if (workMode=="glm") { |
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252 if (roastOpt == "yes") { |
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253 if (is.na(match("Gene", colnames(anno)))) { |
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254 tempStr <- paste("Gene-wise tests selected but'Gene' column not", |
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255 "specified in hairpin annotation file") |
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256 stop(tempStr) |
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257 } |
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258 } |
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259 } |
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260 |
| 2 | 261 ################################################################################ |
| 262 | |
| 263 # Process arguments | |
| 264 if (workMode=="glm") { | |
| 265 if (roastOpt=="yes") { | |
| 266 wantRoast <- TRUE | |
| 267 } else { | |
| 268 wantRoast <- FALSE | |
| 269 } | |
| 270 } | |
| 271 | |
| 272 # Split up contrasts seperated by comma into a vector and replace spaces with | |
| 273 # periods | |
| 274 if (exists("contrastData")) { | |
| 275 contrastData <- unlist(strsplit(contrastData, split=",")) | |
| 276 contrastData <- sanitiseEquation(contrastData) | |
| 277 contrastData <- gsub(" ", ".", contrastData, fixed=TRUE) | |
| 278 } | |
| 279 | |
| 280 # Replace spaces with periods in pair data | |
| 281 if (exists("pairData")) { | |
| 282 pairData <- make.names(pairData) | |
| 283 } | |
| 284 | |
| 285 # Generate output folder and paths | |
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286 dir.create(folderPath, showWarnings=FALSE) |
| 2 | 287 |
| 288 # Generate links for outputs | |
| 289 imgOut("barHairpin") | |
| 290 imgOut("barIndex") | |
| 291 imgOut("mds") | |
| 292 imgOut("bcv") | |
| 293 if (workMode == "classic") { | |
| 294 smearPng <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").png")) | |
| 295 smearPdf <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").pdf")) | |
| 296 topOut <- makeOut(paste0("toptag(", pairData[2], "-", pairData[1],").tsv")) | |
| 297 } else if (workMode=="glm") { | |
| 298 smearPng <- character() | |
| 299 smearPdf <- character() | |
| 300 topOut <- character() | |
| 301 roastOut <- character() | |
| 302 barcodePng <- character() | |
| 303 barcodePdf <- character() | |
| 304 for (i in 1:length(contrastData)) { | |
| 305 smearPng[i] <- makeOut(paste0("smear(", contrastData[i], ").png")) | |
| 306 smearPdf[i] <- makeOut(paste0("smear(", contrastData[i], ").pdf")) | |
| 307 topOut[i] <- makeOut(paste0("toptag(", contrastData[i], ").tsv")) | |
| 308 roastOut[i] <- makeOut(paste0("roast(", contrastData[i], ").tsv")) | |
| 309 barcodePng[i] <- makeOut(paste0("barcode(", contrastData[i], ").png")) | |
| 310 barcodePdf[i] <- makeOut(paste0("barcode(", contrastData[i], ").pdf")) | |
| 311 } | |
| 312 } | |
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313 countsOut <- makeOut("counts.tsv") |
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314 |
| 2 | 315 # Initialise data for html links and images, table with the link label and |
| 316 # link address | |
| 317 linkData <- data.frame(Label=character(), Link=character(), | |
| 318 stringsAsFactors=FALSE) | |
| 319 imageData <- data.frame(Label=character(), Link=character(), | |
| 320 stringsAsFactors=FALSE) | |
| 7 | 321 |
| 322 # Initialise vectors for storage of up/down/neutral regulated counts | |
| 323 upCount <- numeric() | |
| 324 downCount <- numeric() | |
| 325 flatCount <- numeric() | |
| 326 | |
| 2 | 327 ################################################################################ |
| 328 ### Data Processing | |
| 329 ################################################################################ | |
| 330 | |
| 331 # Transform gene selection from string into index values for mroast | |
| 332 if (workMode=="glm") { | |
| 333 if (selectOpt=="rank") { | |
| 334 selectVals <- gsub(" ", "", selectVals, fixed=TRUE) | |
| 335 selectVals <- unlist(strsplit(selectVals, ",")) | |
| 336 | |
| 337 for (i in 1:length(selectVals)) { | |
| 338 if (grepl(":", selectVals[i], fixed=TRUE)) { | |
| 339 temp <- unlist(strsplit(selectVals[i], ":")) | |
| 340 selectVals <- selectVals[-i] | |
| 341 a <- as.numeric(temp[1]) | |
| 342 b <- as.numeric(temp[2]) | |
| 343 selectVals <- c(selectVals, a:b) | |
| 344 } | |
| 345 } | |
| 346 selectVals <- as.numeric(unique(selectVals)) | |
| 347 } else { | |
| 348 selectVals <- gsub(" ", "", selectVals, fixed=TRUE) | |
| 349 selectVals <- unlist(strsplit(selectVals, " ")) | |
| 350 } | |
| 351 } | |
| 352 | |
| 353 if (inputType=="fastq") { | |
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354 # Use EdgeR hairpin process and capture outputs |
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355 hpReadout <- capture.output( |
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356 data <- processHairpinReads(fastqPath, samplePath, annoPath, |
| 2 | 357 hairpinStart=hpStart, hairpinEnd=hpEnd, |
| 358 verbose=TRUE) | |
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359 ) |
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360 |
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361 # Remove function output entries that show processing data or is empty |
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362 hpReadout <- hpReadout[hpReadout!=""] |
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363 hpReadout <- hpReadout[!grepl("Processing", hpReadout)] |
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364 hpReadout <- hpReadout[!grepl("in file", hpReadout)] |
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365 hpReadout <- gsub(" -- ", "", hpReadout, fixed=TRUE) |
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366 |
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367 # Make the names of groups syntactically valid (replace spaces with periods) |
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368 data$samples$group <- make.names(data$samples$group) |
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369 } else if (inputType=="counts") { |
| 2 | 370 # Process counts information, set ID column to be row names |
| 371 rownames(counts) <- counts$ID | |
| 372 counts <- counts[ , !(colnames(counts)=="ID")] | |
| 373 countsRows <- nrow(counts) | |
| 374 | |
| 375 # Process group information | |
| 376 factors <- samples$group[match(samples$ID, colnames(counts))] | |
| 377 annoRows <- nrow(anno) | |
| 378 anno <- anno[match(rownames(counts), anno$ID), ] | |
| 379 annoMatched <- sum(!is.na(anno$ID)) | |
| 380 | |
| 381 if (any(is.na(anno$ID))) { | |
| 382 warningStr <- paste("count table contained more hairpins than", | |
| 383 "specified in hairpin annotation file") | |
| 384 warning(warningStr) | |
| 385 } | |
| 386 | |
| 387 # Filter out rows with zero counts | |
| 388 sel <- rowSums(counts)!=0 | |
| 389 counts <- counts[sel, ] | |
| 390 anno <- anno[sel, ] | |
| 391 | |
| 392 # Create DGEList | |
| 393 data <- DGEList(counts=counts, lib.size=colSums(counts), | |
| 394 norm.factors=rep(1,ncol(counts)), genes=anno, group=factors) | |
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395 |
| 2 | 396 # Make the names of groups syntactically valid (replace spaces with periods) |
| 397 data$samples$group <- make.names(data$samples$group) | |
| 398 } | |
| 399 | |
| 400 # Filter hairpins with low counts | |
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401 preFilterCount <- nrow(data) |
| 2 | 402 sel <- rowSums(cpm(data$counts) > cpmReq) >= sampleReq |
| 403 data <- data[sel, ] | |
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404 postFilterCount <- nrow(data) |
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405 filteredCount <- preFilterCount-postFilterCount |
| 2 | 406 |
| 407 # Estimate dispersions | |
| 408 data <- estimateDisp(data) | |
| 409 commonBCV <- sqrt(data$common.dispersion) | |
| 410 | |
| 411 ################################################################################ | |
| 412 ### Output Processing | |
| 413 ################################################################################ | |
| 414 | |
| 415 # Plot number of hairpins that could be matched per sample | |
| 416 png(barIndexPng, width=600, height=600) | |
| 417 barplot(height<-colSums(data$counts), las=2, main="Counts per index", | |
| 418 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
| 419 imageData[1, ] <- c("Counts per Index", "barIndex.png") | |
| 420 invisible(dev.off()) | |
| 421 | |
| 422 pdf(barIndexPdf) | |
| 423 barplot(height<-colSums(data$counts), las=2, main="Counts per index", | |
| 424 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
| 425 linkData[1, ] <- c("Counts per Index Barplot (.pdf)", "barIndex.pdf") | |
| 426 invisible(dev.off()) | |
| 427 | |
| 428 # Plot per hairpin totals across all samples | |
| 429 png(barHairpinPng, width=600, height=600) | |
| 430 if (nrow(data$counts)<50) { | |
| 431 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
| 432 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
| 433 } else { | |
| 434 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
| 435 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), | |
| 436 names.arg=FALSE) | |
| 437 } | |
| 438 imageData <- rbind(imageData, c("Counts per Hairpin", "barHairpin.png")) | |
| 439 invisible(dev.off()) | |
| 440 | |
| 441 pdf(barHairpinPdf) | |
| 442 if (nrow(data$counts)<50) { | |
| 443 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
| 444 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
| 445 } else { | |
| 446 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
| 447 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), | |
| 448 names.arg=FALSE) | |
| 449 } | |
| 450 newEntry <- c("Counts per Hairpin Barplot (.pdf)", "barHairpin.pdf") | |
| 451 linkData <- rbind(linkData, newEntry) | |
| 452 invisible(dev.off()) | |
| 453 | |
| 454 # Make an MDS plot to visualise relationships between replicate samples | |
| 455 png(mdsPng, width=600, height=600) | |
| 456 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), | |
| 457 main="MDS Plot") | |
| 458 imageData <- rbind(imageData, c("MDS Plot", "mds.png")) | |
| 459 invisible(dev.off()) | |
| 460 | |
| 461 pdf(mdsPdf) | |
| 462 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), | |
| 463 main="MDS Plot") | |
| 464 newEntry <- c("MDS Plot (.pdf)", "mds.pdf") | |
| 465 linkData <- rbind(linkData, newEntry) | |
| 466 invisible(dev.off()) | |
| 467 | |
| 468 if (workMode=="classic") { | |
| 469 # Assess differential representation using classic exact testing methodology | |
| 470 # in edgeR | |
| 471 testData <- exactTest(data, pair=pairData) | |
| 472 | |
| 473 top <- topTags(testData, n=Inf) | |
| 474 topIDs <- top$table[(top$table$FDR < fdrThresh) & | |
| 475 (abs(top$table$logFC) > lfcThresh), 1] | |
| 7 | 476 |
| 2 | 477 write.table(top, file=topOut, row.names=FALSE, sep="\t") |
| 7 | 478 |
| 2 | 479 linkName <- paste0("Top Tags Table(", pairData[2], "-", pairData[1], |
| 480 ") (.tsv)") | |
| 481 linkAddr <- paste0("toptag(", pairData[2], "-", pairData[1], ").tsv") | |
| 482 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
| 483 | |
| 7 | 484 upCount[1] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) |
| 485 downCount[1] <- sum(top$table$FDR < fdrThresh & | |
| 486 top$table$logFC < -lfcThresh) | |
| 487 flatCount[1] <- sum(top$table$FDR > fdrThresh | | |
| 488 abs(top$table$logFC) < lfcThresh) | |
| 489 | |
| 490 | |
| 491 | |
| 2 | 492 # Select hairpins with FDR < 0.05 to highlight on plot |
| 493 png(smearPng, width=600, height=600) | |
| 494 plotTitle <- gsub(".", " ", | |
| 495 paste0("Smear Plot: ", pairData[2], "-", pairData[1]), | |
| 7 | 496 fixed=TRUE) |
| 2 | 497 plotSmear(testData, de.tags=topIDs, |
| 498 pch=20, cex=1.0, main=plotTitle) | |
| 7 | 499 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) |
| 2 | 500 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ")") |
| 501 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1],").png") | |
| 502 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
| 503 invisible(dev.off()) | |
| 504 | |
| 505 pdf(smearPdf) | |
| 506 plotTitle <- gsub(".", " ", | |
| 507 paste0("Smear Plot: ", pairData[2], "-", pairData[1]), | |
| 7 | 508 fixed=TRUE) |
| 2 | 509 plotSmear(testData, de.tags=topIDs, |
| 510 pch=20, cex=1.0, main=plotTitle) | |
| 7 | 511 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) |
| 2 | 512 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ") (.pdf)") |
| 513 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1], ").pdf") | |
| 514 linkData <- rbind(linkData, c(imgName, imgAddr)) | |
| 515 invisible(dev.off()) | |
| 7 | 516 |
| 2 | 517 } else if (workMode=="glm") { |
| 518 # Generating design information | |
| 519 factors <- factor(data$sample$group) | |
| 520 design <- model.matrix(~0+factors) | |
| 521 | |
| 522 colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE) | |
| 523 | |
| 524 # Split up contrasts seperated by comma into a vector | |
| 525 contrastData <- unlist(strsplit(contrastData, split=",")) | |
| 7 | 526 |
| 2 | 527 for (i in 1:length(contrastData)) { |
| 528 # Generate contrasts information | |
| 529 contrasts <- makeContrasts(contrasts=contrastData[i], levels=design) | |
| 530 | |
| 531 # Fit negative bionomial GLM | |
| 7 | 532 fit <- glmFit(data, design) |
| 2 | 533 # Carry out Likelihood ratio test |
| 7 | 534 testData <- glmLRT(fit, contrast=contrasts) |
| 2 | 535 |
| 536 # Select hairpins with FDR < 0.05 to highlight on plot | |
| 537 top <- topTags(testData, n=Inf) | |
| 538 topIDs <- top$table[(top$table$FDR < fdrThresh) & | |
| 539 (abs(top$table$logFC) > lfcThresh), 1] | |
| 540 write.table(top, file=topOut[i], row.names=FALSE, sep="\t") | |
| 541 | |
| 542 linkName <- paste0("Top Tags Table(", contrastData[i], ") (.tsv)") | |
| 543 linkAddr <- paste0("toptag(", contrastData[i], ").tsv") | |
| 544 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
| 545 | |
| 7 | 546 # Collect counts for differential representation |
| 547 upCount[i] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) | |
| 548 downCount[i] <- sum(top$table$FDR < fdrThresh & | |
| 549 top$table$logFC < -lfcThresh) | |
| 550 flatCount[i] <- sum(top$table$FDR > fdrThresh | | |
| 551 abs(top$table$logFC) < lfcThresh) | |
| 552 | |
| 2 | 553 # Make a plot of logFC versus logCPM |
| 554 png(smearPng[i], height=600, width=600) | |
| 555 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], | |
| 556 fixed=TRUE)) | |
| 557 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) | |
| 558 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) | |
| 559 | |
| 560 imgName <- paste0("Smear Plot(", contrastData[i], ")") | |
| 561 imgAddr <- paste0("smear(", contrastData[i], ").png") | |
| 562 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
| 563 invisible(dev.off()) | |
| 564 | |
| 565 pdf(smearPdf[i]) | |
| 566 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], | |
| 567 fixed=TRUE)) | |
| 568 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) | |
| 569 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) | |
| 570 | |
| 571 linkName <- paste0("Smear Plot(", contrastData[i], ") (.pdf)") | |
| 572 linkAddr <- paste0("smear(", contrastData[i], ").pdf") | |
| 573 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
| 574 invisible(dev.off()) | |
| 575 | |
| 576 genes <- as.character(data$genes$Gene) | |
| 577 unq <- unique(genes) | |
| 578 unq <- unq[!is.na(unq)] | |
| 579 geneList <- list() | |
| 580 for (gene in unq) { | |
| 581 if (length(which(genes==gene)) >= hairpinReq) { | |
| 582 geneList[[gene]] <- which(genes==gene) | |
| 583 } | |
| 584 } | |
| 585 | |
| 586 if (wantRoast) { | |
| 587 # Input preparaton for roast | |
| 7 | 588 nrot <- 9999 |
| 2 | 589 set.seed(602214129) |
| 590 roastData <- mroast(data, index=geneList, design=design, | |
| 591 contrast=contrasts, nrot=nrot) | |
| 592 roastData <- cbind(GeneID=rownames(roastData), roastData) | |
| 593 write.table(roastData, file=roastOut[i], row.names=FALSE, sep="\t") | |
| 594 linkName <- paste0("Gene Level Analysis Table(", contrastData[i], | |
| 595 ") (.tsv)") | |
| 596 linkAddr <- paste0("roast(", contrastData[i], ").tsv") | |
| 597 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
| 598 if (selectOpt=="rank") { | |
| 599 selectedGenes <- rownames(roastData)[selectVals] | |
| 600 } else { | |
| 601 selectedGenes <- selectVals | |
| 602 } | |
| 603 | |
| 604 if (packageVersion("limma")<"3.19.19") { | |
| 605 png(barcodePng[i], width=600, height=length(selectedGenes)*150) | |
| 606 } else { | |
| 607 png(barcodePng[i], width=600, height=length(selectedGenes)*300) | |
| 608 } | |
| 609 par(mfrow=c(length(selectedGenes), 1)) | |
| 610 for (gene in selectedGenes) { | |
| 611 barcodeplot(testData$table$logFC, index=geneList[[gene]], | |
| 612 main=paste("Barcode Plot for", gene, "(logFCs)", | |
| 613 gsub(".", " ", contrastData[i])), | |
| 614 labels=c("Positive logFC", "Negative logFC")) | |
| 615 } | |
| 616 imgName <- paste0("Barcode Plot(", contrastData[i], ")") | |
| 617 imgAddr <- paste0("barcode(", contrastData[i], ").png") | |
| 618 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
| 619 dev.off() | |
| 620 if (packageVersion("limma")<"3.19.19") { | |
| 621 pdf(barcodePdf[i], width=8, height=2) | |
| 622 } else { | |
| 623 pdf(barcodePdf[i], width=8, height=4) | |
| 624 } | |
| 625 for (gene in selectedGenes) { | |
| 626 barcodeplot(testData$table$logFC, index=geneList[[gene]], | |
| 627 main=paste("Barcode Plot for", gene, "(logFCs)", | |
| 628 gsub(".", " ", contrastData[i])), | |
| 629 labels=c("Positive logFC", "Negative logFC")) | |
| 630 } | |
| 631 linkName <- paste0("Barcode Plot(", contrastData[i], ") (.pdf)") | |
| 632 linkAddr <- paste0("barcode(", contrastData[i], ").pdf") | |
| 633 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
| 634 dev.off() | |
| 635 } | |
| 636 } | |
| 637 } | |
| 638 | |
| 7 | 639 sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount) |
| 640 if (workMode == "glm") { | |
| 641 row.names(sigDiff) <- contrastData | |
| 642 } else if (workMode == "classic") { | |
| 643 row.names(sigDiff) <- paste0(pairData[2], "-", pairData[1]) | |
| 644 } | |
| 645 | |
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646 ID <- rownames(data$counts) |
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647 outputCounts <- cbind(ID, data$counts) |
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648 write.table(outputCounts, file=countsOut, row.names=FALSE, sep="\t") |
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649 linkName <- "Counts table (.tsv)" |
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650 linkAddr <- "counts.tsv" |
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651 linkData <- rbind(linkData, c(linkName, linkAddr)) |
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652 |
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653 # Record ending time and calculate total run time |
| 2 | 654 timeEnd <- as.character(Sys.time()) |
|
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655 timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3)) |
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656 timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE) |
| 2 | 657 ################################################################################ |
| 658 ### HTML Generation | |
| 659 ################################################################################ | |
| 660 # Clear file | |
| 661 cat("", file=htmlPath) | |
| 662 | |
| 663 cata("<html>\n") | |
| 664 HtmlHead("EdgeR Output") | |
| 665 | |
| 666 cata("<body>\n") | |
| 667 cata("<h3>EdgeR Analysis Output:</h3>\n") | |
| 668 cata("<h4>Input Summary:</h4>\n") | |
| 669 if (inputType=="fastq") { | |
| 670 cata("<ul>\n") | |
| 671 ListItem(hpReadout[1]) | |
| 672 ListItem(hpReadout[2]) | |
| 673 cata("</ul>\n") | |
|
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674 cata(hpReadout[3], "<br />\n") |
| 2 | 675 cata("<ul>\n") |
| 676 ListItem(hpReadout[4]) | |
| 677 ListItem(hpReadout[7]) | |
| 678 cata("</ul>\n") | |
|
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679 cata(hpReadout[8:11], sep="<br />\n") |
| 2 | 680 cata("<br />\n") |
| 681 cata("<b>Please check that read percentages are consistent with ") | |
| 682 cata("expectations.</b><br >\n") | |
| 683 } else if (inputType=="counts") { | |
| 684 cata("<ul>\n") | |
| 685 ListItem("Number of Samples: ", ncol(data$counts)) | |
| 686 ListItem("Number of Hairpins: ", countsRows) | |
| 687 ListItem("Number of annotations provided: ", annoRows) | |
| 688 ListItem("Number of annotations matched to hairpin: ", annoMatched) | |
| 689 cata("</ul>\n") | |
| 690 } | |
| 691 | |
| 692 cata("The estimated common biological coefficient of variation (BCV) is: ", | |
| 693 commonBCV, "<br />\n") | |
| 694 | |
| 695 cata("<h4>Output:</h4>\n") | |
| 7 | 696 cata("PDF copies of JPEGS available in 'Plots' section.<br />\n") |
| 2 | 697 for (i in 1:nrow(imageData)) { |
| 698 if (grepl("barcode", imageData$Link[i])) { | |
| 699 if (packageVersion("limma")<"3.19.19") { | |
| 700 HtmlImage(imageData$Link[i], imageData$Label[i], | |
| 701 height=length(selectedGenes)*150) | |
| 702 } else { | |
| 703 HtmlImage(imageData$Link[i], imageData$Label[i], | |
| 704 height=length(selectedGenes)*300) | |
| 705 } | |
| 706 } else { | |
| 707 HtmlImage(imageData$Link[i], imageData$Label[i]) | |
| 708 } | |
| 709 } | |
|
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710 cata("<br />\n") |
| 2 | 711 |
| 7 | 712 cata("<h4>Differential Representation Counts:</h4>\n") |
| 713 | |
| 714 cata("<table border=\"1\" cellpadding=\"4\">\n") | |
| 715 cata("<tr>\n") | |
| 716 TableItem() | |
| 717 for (i in colnames(sigDiff)) { | |
| 718 TableHeadItem(i) | |
| 719 } | |
| 720 cata("</tr>\n") | |
| 721 for (i in 1:nrow(sigDiff)) { | |
| 722 cata("<tr>\n") | |
| 723 TableHeadItem(unmake.names(row.names(sigDiff)[i])) | |
| 724 for (j in 1:ncol(sigDiff)) { | |
| 725 TableItem(as.character(sigDiff[i, j])) | |
| 726 } | |
| 727 cata("</tr>\n") | |
| 728 } | |
| 729 cata("</table>") | |
| 730 | |
| 2 | 731 cata("<h4>Plots:</h4>\n") |
| 732 for (i in 1:nrow(linkData)) { | |
| 733 if (!grepl(".tsv", linkData$Link[i])) { | |
| 734 HtmlLink(linkData$Link[i], linkData$Label[i]) | |
| 735 } | |
| 736 } | |
| 737 | |
| 738 cata("<h4>Tables:</h4>\n") | |
| 739 for (i in 1:nrow(linkData)) { | |
| 740 if (grepl(".tsv", linkData$Link[i])) { | |
| 741 HtmlLink(linkData$Link[i], linkData$Label[i]) | |
| 742 } | |
| 743 } | |
| 744 | |
| 7 | 745 cata("<p>Alt-click links to download file.</p>\n") |
| 746 cata("<p>Click floppy disc icon associated history item to download ") | |
| 747 cata("all files.</p>\n") | |
| 748 cata("<p>.tsv files can be viewed in Excel or any spreadsheet program.</p>\n") | |
|
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749 |
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750 cata("<h4>Additional Information:</h4>\n") |
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751 |
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752 if (inputType == "fastq") { |
|
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753 ListItem("Data was gathered from fastq raw read file(s).") |
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754 } else if (inputType == "counts") { |
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755 ListItem("Data was gathered from a table of counts.") |
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756 } |
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757 |
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758 if (cpmReq!=0 && sampleReq!=0) { |
| 7 | 759 tempStr <- paste("Hairpins with less than", cpmReq, |
| 760 "CPM in at least", sampleReq, "samples are insignificant", | |
| 761 "and filtered out.") | |
|
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762 ListItem(tempStr) |
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763 filterProp <- round(filteredCount/preFilterCount*100, digits=2) |
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764 tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp, |
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765 "%) hairpins were filtered out for low count-per-million.") |
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766 ListItem(tempStr) |
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767 } |
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768 |
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769 if (workMode == "classic") { |
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770 ListItem("An exact test was performed on each hairpin.") |
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771 } else if (workMode == "glm") { |
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772 ListItem("A generalised linear model was fitted to each hairpin.") |
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773 } |
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774 |
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775 cit <- character() |
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776 link <-character() |
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777 link[1] <- paste0("<a href=\"", |
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778 "http://www.bioconductor.org/packages/release/bioc/", |
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779 "vignettes/limma/inst/doc/usersguide.pdf", |
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780 "\">", "limma User's Guide", "</a>.") |
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781 link[2] <- paste0("<a href=\"", |
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782 "http://www.bioconductor.org/packages/release/bioc/", |
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783 "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf", |
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784 "\">", "edgeR User's Guide", "</a>") |
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785 |
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786 cit[1] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010).", |
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787 "edgeR: a Bioconductor package for differential", |
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788 "expression analysis of digital gene expression", |
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789 "data. Bioinformatics 26, 139-140") |
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790 cit[2] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests", |
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791 "for assessing differences in tag abundance. Bioinformatics", |
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diff
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|
792 "23, 2881-2887") |
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793 cit[3] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of", |
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794 "negative binomial dispersion, with applications to SAGE data.", |
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diff
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|
795 "Biostatistics, 9, 321-332") |
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|
796 |
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797 cit[4] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential", |
|
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|
798 "expression analysis of multifactor RNA-Seq experiments with", |
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diff
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|
799 "respect to biological variation. Nucleic Acids Research 40,", |
|
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diff
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|
800 "4288-4297") |
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|
801 |
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|
802 cata("<h4>Citations</h4>") |
|
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|
803 cata("<ol>\n") |
|
3d04308a99f9
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|
804 ListItem(cit[1]) |
|
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diff
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|
805 ListItem(cit[2]) |
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|
806 ListItem(cit[3]) |
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diff
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|
807 ListItem(cit[4]) |
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diff
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|
808 cata("</ol>\n") |
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|
809 |
| 2 | 810 cata("<table border=\"0\">\n") |
| 811 cata("<tr>\n") | |
| 812 TableItem("Task started at:"); TableItem(timeStart) | |
| 813 cata("</tr>\n") | |
| 814 cata("<tr>\n") | |
| 815 TableItem("Task ended at:"); TableItem(timeEnd) | |
| 816 cata("</tr>\n") | |
|
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diff
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817 cata("<tr>\n") |
|
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818 TableItem("Task run time:"); TableItem(timeTaken) |
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819 cata("<tr>\n") |
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820 cata("</table>\n") |
| 2 | 821 |
| 822 cata("</body>\n") | |
| 823 cata("</html>") |