Mercurial > repos > shians > shrnaseq
annotate hairpinTool.R @ 8:548802b3492f
Version 1.0.9
- Added R session info to output
- Added email for bug reports
- Changed edgeR requirement to be stopping condition
- Changed names of gene-wise comparison tables to "gene_level" from "roast"
- Fixed selection of multiple genes for barcode plotting
author | shian_su <registertonysu@gmail.com> |
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date | Fri, 02 May 2014 17:22:24 +1000 |
parents | 91e411fcdecc |
children | f1076bfb0ed1 |
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") { | |
8 | 61 stop("Please update 'edgeR' to version >= 3.5.23 to run this tool") |
62 } | |
63 | |
64 if (packageVersion("limma")<"3.19.19") { | |
65 message("Update 'limma' to version >= 3.19.19 to see updated barcode graphs") | |
2 | 66 } |
67 | |
68 ################################################################################ | |
69 ### Function declarations | |
70 ################################################################################ | |
71 | |
7 | 72 # Function to load libaries without messages |
73 silentLibrary <- function(...) { | |
74 list <- c(...) | |
75 for (package in list){ | |
76 suppressPackageStartupMessages(library(package, character.only=TRUE)) | |
77 } | |
78 } | |
79 | |
2 | 80 # Function to sanitise contrast equations so there are no whitespaces |
81 # surrounding the arithmetic operators, leading or trailing whitespace | |
82 sanitiseEquation <- function(equation) { | |
83 equation <- gsub(" *[+] *", "+", equation) | |
84 equation <- gsub(" *[-] *", "-", equation) | |
85 equation <- gsub(" *[/] *", "/", equation) | |
86 equation <- gsub(" *[*] *", "*", equation) | |
87 equation <- gsub("^\\s+|\\s+$", "", equation) | |
88 return(equation) | |
89 } | |
90 | |
91 # Function to sanitise group information | |
92 sanitiseGroups <- function(string) { | |
93 string <- gsub(" *[,] *", ",", string) | |
94 string <- gsub("^\\s+|\\s+$", "", string) | |
95 return(string) | |
96 } | |
97 | |
98 # Function to change periods to whitespace in a string | |
99 unmake.names <- function(string) { | |
100 string <- gsub(".", " ", string, fixed=TRUE) | |
101 return(string) | |
102 } | |
103 | |
104 # Function has string input and generates an output path string | |
105 makeOut <- function(filename) { | |
106 return(paste0(folderPath, "/", filename)) | |
107 } | |
108 | |
109 # Function has string input and generates both a pdf and png output strings | |
110 imgOut <- function(filename) { | |
111 assign(paste0(filename, "Png"), makeOut(paste0(filename,".png")), | |
7 | 112 envir=.GlobalEnv) |
2 | 113 assign(paste0(filename, "Pdf"), makeOut(paste0(filename,".pdf")), |
7 | 114 envir=.GlobalEnv) |
2 | 115 } |
116 | |
117 # Create cat function default path set, default seperator empty and appending | |
118 # true by default (Ripped straight from the cat function with altered argument | |
119 # defaults) | |
7 | 120 cata <- function(..., file=htmlPath, sep="", fill=FALSE, labels=NULL, |
121 append=TRUE) { | |
2 | 122 if (is.character(file)) |
123 if (file == "") | |
124 file <- stdout() | |
125 else if (substring(file, 1L, 1L) == "|") { | |
126 file <- pipe(substring(file, 2L), "w") | |
127 on.exit(close(file)) | |
128 } | |
129 else { | |
130 file <- file(file, ifelse(append, "a", "w")) | |
131 on.exit(close(file)) | |
132 } | |
133 .Internal(cat(list(...), file, sep, fill, labels, append)) | |
134 } | |
135 | |
136 # Function to write code for html head and title | |
137 HtmlHead <- function(title) { | |
138 cata("<head>\n") | |
139 cata("<title>", title, "</title>\n") | |
140 cata("</head>\n") | |
141 } | |
142 | |
143 # Function to write code for html links | |
144 HtmlLink <- function(address, label=address) { | |
145 cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n") | |
146 } | |
147 | |
148 # Function to write code for html images | |
149 HtmlImage <- function(source, label=source, height=600, width=600) { | |
150 cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height) | |
151 cata("\" width=\"", width, "\"/>\n") | |
152 } | |
153 | |
154 # Function to write code for html list items | |
155 ListItem <- function(...) { | |
156 cata("<li>", ..., "</li>\n") | |
157 } | |
158 | |
159 TableItem <- function(...) { | |
160 cata("<td>", ..., "</td>\n") | |
161 } | |
162 | |
163 TableHeadItem <- function(...) { | |
164 cata("<th>", ..., "</th>\n") | |
165 } | |
166 ################################################################################ | |
167 ### Input Processing | |
168 ################################################################################ | |
169 | |
170 # Grabbing arguments from command line | |
171 argv <- commandArgs(TRUE) | |
172 | |
173 # Remove fastq file paths after collecting from argument vector | |
174 inputType <- as.character(argv[1]) | |
175 if (inputType=="fastq") { | |
176 fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)], | |
177 fixed=TRUE)) | |
178 argv <- argv[!grepl("fastq::", argv, fixed=TRUE)] | |
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179 annoPath <- as.character(argv[2]) |
2 | 180 samplePath <- as.character(argv[3]) |
181 barStart <- as.numeric(argv[4]) | |
182 barEnd <- as.numeric(argv[5]) | |
183 hpStart <- as.numeric(argv[6]) | |
184 hpEnd <- as.numeric(argv[7]) | |
185 } else if (inputType=="counts") { | |
186 countPath <- as.character(argv[2]) | |
187 annoPath <- as.character(argv[3]) | |
188 samplePath <- as.character(argv[4]) | |
189 } | |
190 | |
191 cpmReq <- as.numeric(argv[8]) | |
192 sampleReq <- as.numeric(argv[9]) | |
193 fdrThresh <- as.numeric(argv[10]) | |
194 lfcThresh <- as.numeric(argv[11]) | |
195 workMode <- as.character(argv[12]) | |
196 htmlPath <- as.character(argv[13]) | |
197 folderPath <- as.character(argv[14]) | |
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198 |
2 | 199 if (workMode=="classic") { |
200 pairData <- character() | |
201 pairData[2] <- as.character(argv[15]) | |
202 pairData[1] <- as.character(argv[16]) | |
203 } else if (workMode=="glm") { | |
204 contrastData <- as.character(argv[15]) | |
205 roastOpt <- as.character(argv[16]) | |
206 hairpinReq <- as.numeric(argv[17]) | |
207 selectOpt <- as.character(argv[18]) | |
208 selectVals <- as.character(argv[19]) | |
209 } | |
210 | |
211 # Read in inputs | |
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212 |
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213 samples <- read.table(samplePath, header=TRUE, sep="\t") |
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214 anno <- read.table(annoPath, header=TRUE, sep="\t") |
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215 if (inputType=="counts") { |
2 | 216 counts <- read.table(countPath, header=TRUE, sep="\t") |
217 } | |
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218 |
2 | 219 ###################### Check inputs for correctness ############################ |
220 samples$ID <- make.names(samples$ID) | |
221 | |
222 if (!any(grepl("group", names(samples)))) { | |
223 stop("'group' column not specified in sample annotation file") | |
224 } # Check if grouping variable has been specified | |
225 | |
226 if (any(table(samples$ID)>1)){ | |
227 tab <- table(samples$ID) | |
228 offenders <- paste(names(tab[tab>1]), collapse=", ") | |
229 offenders <- unmake.names(offenders) | |
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230 stop("'ID' column of sample annotation must have unique values, values ", |
2 | 231 offenders, " are repeated") |
232 } # Check that IDs in sample annotation are unique | |
233 | |
234 if (inputType=="fastq") { | |
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235 |
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236 if (any(table(anno$ID)>1)){ |
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237 tab <- table(anno$ID) |
2 | 238 offenders <- paste(names(tab[tab>1]), collapse=", ") |
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239 stop("'ID' column of hairpin annotation must have unique values, values ", |
2 | 240 offenders, " are repeated") |
241 } # Check that IDs in hairpin annotation are unique | |
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242 |
2 | 243 } else if (inputType=="counts") { |
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244 if (any(is.na(match(samples$ID, colnames(counts))))) { |
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245 stop("not all samples have groups specified") |
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246 } # Check that a group has be specifed for each sample |
2 | 247 |
248 if (any(table(counts$ID)>1)){ | |
249 tab <- table(counts$ID) | |
250 offenders <- paste(names(tab[tab>1]), collapse=", ") | |
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251 stop("'ID' column of count table must have unique values, values ", |
2 | 252 offenders, " are repeated") |
253 } # Check that IDs in count table are unique | |
254 } | |
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255 if (workMode=="glm") { |
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256 if (roastOpt == "yes") { |
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257 if (is.na(match("Gene", colnames(anno)))) { |
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258 tempStr <- paste("Gene-wise tests selected but'Gene' column not", |
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259 "specified in hairpin annotation file") |
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260 stop(tempStr) |
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261 } |
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262 } |
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263 } |
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264 |
2 | 265 ################################################################################ |
266 | |
267 # Process arguments | |
268 if (workMode=="glm") { | |
269 if (roastOpt=="yes") { | |
270 wantRoast <- TRUE | |
271 } else { | |
272 wantRoast <- FALSE | |
273 } | |
274 } | |
275 | |
276 # Split up contrasts seperated by comma into a vector and replace spaces with | |
277 # periods | |
278 if (exists("contrastData")) { | |
279 contrastData <- unlist(strsplit(contrastData, split=",")) | |
280 contrastData <- sanitiseEquation(contrastData) | |
281 contrastData <- gsub(" ", ".", contrastData, fixed=TRUE) | |
282 } | |
283 | |
284 # Replace spaces with periods in pair data | |
285 if (exists("pairData")) { | |
286 pairData <- make.names(pairData) | |
287 } | |
288 | |
289 # Generate output folder and paths | |
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290 dir.create(folderPath, showWarnings=FALSE) |
2 | 291 |
292 # Generate links for outputs | |
293 imgOut("barHairpin") | |
294 imgOut("barIndex") | |
295 imgOut("mds") | |
296 imgOut("bcv") | |
297 if (workMode == "classic") { | |
298 smearPng <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").png")) | |
299 smearPdf <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").pdf")) | |
300 topOut <- makeOut(paste0("toptag(", pairData[2], "-", pairData[1],").tsv")) | |
301 } else if (workMode=="glm") { | |
302 smearPng <- character() | |
303 smearPdf <- character() | |
304 topOut <- character() | |
305 roastOut <- character() | |
306 barcodePng <- character() | |
307 barcodePdf <- character() | |
308 for (i in 1:length(contrastData)) { | |
309 smearPng[i] <- makeOut(paste0("smear(", contrastData[i], ").png")) | |
310 smearPdf[i] <- makeOut(paste0("smear(", contrastData[i], ").pdf")) | |
311 topOut[i] <- makeOut(paste0("toptag(", contrastData[i], ").tsv")) | |
8 | 312 roastOut[i] <- makeOut(paste0("gene_level(", contrastData[i], ").tsv")) |
2 | 313 barcodePng[i] <- makeOut(paste0("barcode(", contrastData[i], ").png")) |
314 barcodePdf[i] <- makeOut(paste0("barcode(", contrastData[i], ").pdf")) | |
315 } | |
316 } | |
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317 countsOut <- makeOut("counts.tsv") |
8 | 318 sessionOut <- makeOut("session_info.txt") |
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319 |
2 | 320 # Initialise data for html links and images, table with the link label and |
321 # link address | |
322 linkData <- data.frame(Label=character(), Link=character(), | |
323 stringsAsFactors=FALSE) | |
324 imageData <- data.frame(Label=character(), Link=character(), | |
325 stringsAsFactors=FALSE) | |
7 | 326 |
327 # Initialise vectors for storage of up/down/neutral regulated counts | |
328 upCount <- numeric() | |
329 downCount <- numeric() | |
330 flatCount <- numeric() | |
331 | |
2 | 332 ################################################################################ |
333 ### Data Processing | |
334 ################################################################################ | |
335 | |
336 # Transform gene selection from string into index values for mroast | |
337 if (workMode=="glm") { | |
338 if (selectOpt=="rank") { | |
339 selectVals <- gsub(" ", "", selectVals, fixed=TRUE) | |
340 selectVals <- unlist(strsplit(selectVals, ",")) | |
341 | |
342 for (i in 1:length(selectVals)) { | |
343 if (grepl(":", selectVals[i], fixed=TRUE)) { | |
344 temp <- unlist(strsplit(selectVals[i], ":")) | |
345 selectVals <- selectVals[-i] | |
346 a <- as.numeric(temp[1]) | |
347 b <- as.numeric(temp[2]) | |
348 selectVals <- c(selectVals, a:b) | |
349 } | |
350 } | |
351 selectVals <- as.numeric(unique(selectVals)) | |
352 } else { | |
353 selectVals <- gsub(" ", "", selectVals, fixed=TRUE) | |
8 | 354 selectVals <- unlist(strsplit(selectVals, ",")) |
2 | 355 } |
356 } | |
357 | |
358 if (inputType=="fastq") { | |
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359 # Use EdgeR hairpin process and capture outputs |
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360 hpReadout <- capture.output( |
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361 data <- processHairpinReads(fastqPath, samplePath, annoPath, |
2 | 362 hairpinStart=hpStart, hairpinEnd=hpEnd, |
363 verbose=TRUE) | |
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364 ) |
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365 |
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366 # Remove function output entries that show processing data or is empty |
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367 hpReadout <- hpReadout[hpReadout!=""] |
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368 hpReadout <- hpReadout[!grepl("Processing", hpReadout)] |
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369 hpReadout <- hpReadout[!grepl("in file", hpReadout)] |
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370 hpReadout <- gsub(" -- ", "", hpReadout, fixed=TRUE) |
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371 |
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372 # Make the names of groups syntactically valid (replace spaces with periods) |
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373 data$samples$group <- make.names(data$samples$group) |
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374 } else if (inputType=="counts") { |
2 | 375 # Process counts information, set ID column to be row names |
376 rownames(counts) <- counts$ID | |
377 counts <- counts[ , !(colnames(counts)=="ID")] | |
378 countsRows <- nrow(counts) | |
379 | |
380 # Process group information | |
381 factors <- samples$group[match(samples$ID, colnames(counts))] | |
382 annoRows <- nrow(anno) | |
383 anno <- anno[match(rownames(counts), anno$ID), ] | |
384 annoMatched <- sum(!is.na(anno$ID)) | |
385 | |
386 if (any(is.na(anno$ID))) { | |
387 warningStr <- paste("count table contained more hairpins than", | |
388 "specified in hairpin annotation file") | |
389 warning(warningStr) | |
390 } | |
391 | |
392 # Filter out rows with zero counts | |
393 sel <- rowSums(counts)!=0 | |
394 counts <- counts[sel, ] | |
395 anno <- anno[sel, ] | |
396 | |
397 # Create DGEList | |
398 data <- DGEList(counts=counts, lib.size=colSums(counts), | |
399 norm.factors=rep(1,ncol(counts)), genes=anno, group=factors) | |
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400 |
2 | 401 # Make the names of groups syntactically valid (replace spaces with periods) |
402 data$samples$group <- make.names(data$samples$group) | |
403 } | |
404 | |
405 # Filter hairpins with low counts | |
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406 preFilterCount <- nrow(data) |
2 | 407 sel <- rowSums(cpm(data$counts) > cpmReq) >= sampleReq |
408 data <- data[sel, ] | |
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409 postFilterCount <- nrow(data) |
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410 filteredCount <- preFilterCount-postFilterCount |
2 | 411 |
412 # Estimate dispersions | |
413 data <- estimateDisp(data) | |
414 commonBCV <- sqrt(data$common.dispersion) | |
415 | |
416 ################################################################################ | |
417 ### Output Processing | |
418 ################################################################################ | |
419 | |
420 # Plot number of hairpins that could be matched per sample | |
421 png(barIndexPng, width=600, height=600) | |
422 barplot(height<-colSums(data$counts), las=2, main="Counts per index", | |
423 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
424 imageData[1, ] <- c("Counts per Index", "barIndex.png") | |
425 invisible(dev.off()) | |
426 | |
427 pdf(barIndexPdf) | |
428 barplot(height<-colSums(data$counts), las=2, main="Counts per index", | |
429 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
430 linkData[1, ] <- c("Counts per Index Barplot (.pdf)", "barIndex.pdf") | |
431 invisible(dev.off()) | |
432 | |
433 # Plot per hairpin totals across all samples | |
434 png(barHairpinPng, width=600, height=600) | |
435 if (nrow(data$counts)<50) { | |
436 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
437 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
438 } else { | |
439 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
440 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), | |
441 names.arg=FALSE) | |
442 } | |
443 imageData <- rbind(imageData, c("Counts per Hairpin", "barHairpin.png")) | |
444 invisible(dev.off()) | |
445 | |
446 pdf(barHairpinPdf) | |
447 if (nrow(data$counts)<50) { | |
448 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
449 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2)) | |
450 } else { | |
451 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin", | |
452 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2), | |
453 names.arg=FALSE) | |
454 } | |
455 newEntry <- c("Counts per Hairpin Barplot (.pdf)", "barHairpin.pdf") | |
456 linkData <- rbind(linkData, newEntry) | |
457 invisible(dev.off()) | |
458 | |
459 # Make an MDS plot to visualise relationships between replicate samples | |
460 png(mdsPng, width=600, height=600) | |
461 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), | |
462 main="MDS Plot") | |
463 imageData <- rbind(imageData, c("MDS Plot", "mds.png")) | |
464 invisible(dev.off()) | |
465 | |
466 pdf(mdsPdf) | |
467 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), | |
468 main="MDS Plot") | |
469 newEntry <- c("MDS Plot (.pdf)", "mds.pdf") | |
470 linkData <- rbind(linkData, newEntry) | |
471 invisible(dev.off()) | |
472 | |
473 if (workMode=="classic") { | |
474 # Assess differential representation using classic exact testing methodology | |
475 # in edgeR | |
476 testData <- exactTest(data, pair=pairData) | |
477 | |
478 top <- topTags(testData, n=Inf) | |
479 topIDs <- top$table[(top$table$FDR < fdrThresh) & | |
480 (abs(top$table$logFC) > lfcThresh), 1] | |
7 | 481 |
2 | 482 write.table(top, file=topOut, row.names=FALSE, sep="\t") |
7 | 483 |
2 | 484 linkName <- paste0("Top Tags Table(", pairData[2], "-", pairData[1], |
485 ") (.tsv)") | |
486 linkAddr <- paste0("toptag(", pairData[2], "-", pairData[1], ").tsv") | |
487 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
488 | |
7 | 489 upCount[1] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) |
490 downCount[1] <- sum(top$table$FDR < fdrThresh & | |
491 top$table$logFC < -lfcThresh) | |
492 flatCount[1] <- sum(top$table$FDR > fdrThresh | | |
493 abs(top$table$logFC) < lfcThresh) | |
494 | |
495 | |
496 | |
2 | 497 # Select hairpins with FDR < 0.05 to highlight on plot |
498 png(smearPng, width=600, height=600) | |
499 plotTitle <- gsub(".", " ", | |
500 paste0("Smear Plot: ", pairData[2], "-", pairData[1]), | |
7 | 501 fixed=TRUE) |
2 | 502 plotSmear(testData, de.tags=topIDs, |
503 pch=20, cex=1.0, main=plotTitle) | |
7 | 504 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) |
2 | 505 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ")") |
506 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1],").png") | |
507 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
508 invisible(dev.off()) | |
509 | |
510 pdf(smearPdf) | |
511 plotTitle <- gsub(".", " ", | |
512 paste0("Smear Plot: ", pairData[2], "-", pairData[1]), | |
7 | 513 fixed=TRUE) |
2 | 514 plotSmear(testData, de.tags=topIDs, |
515 pch=20, cex=1.0, main=plotTitle) | |
7 | 516 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) |
2 | 517 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ") (.pdf)") |
518 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1], ").pdf") | |
519 linkData <- rbind(linkData, c(imgName, imgAddr)) | |
520 invisible(dev.off()) | |
7 | 521 |
2 | 522 } else if (workMode=="glm") { |
523 # Generating design information | |
524 factors <- factor(data$sample$group) | |
525 design <- model.matrix(~0+factors) | |
526 | |
527 colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE) | |
528 | |
529 # Split up contrasts seperated by comma into a vector | |
530 contrastData <- unlist(strsplit(contrastData, split=",")) | |
7 | 531 |
2 | 532 for (i in 1:length(contrastData)) { |
533 # Generate contrasts information | |
534 contrasts <- makeContrasts(contrasts=contrastData[i], levels=design) | |
535 | |
536 # Fit negative bionomial GLM | |
7 | 537 fit <- glmFit(data, design) |
2 | 538 # Carry out Likelihood ratio test |
7 | 539 testData <- glmLRT(fit, contrast=contrasts) |
2 | 540 |
541 # Select hairpins with FDR < 0.05 to highlight on plot | |
542 top <- topTags(testData, n=Inf) | |
543 topIDs <- top$table[(top$table$FDR < fdrThresh) & | |
544 (abs(top$table$logFC) > lfcThresh), 1] | |
545 write.table(top, file=topOut[i], row.names=FALSE, sep="\t") | |
546 | |
547 linkName <- paste0("Top Tags Table(", contrastData[i], ") (.tsv)") | |
548 linkAddr <- paste0("toptag(", contrastData[i], ").tsv") | |
549 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
550 | |
7 | 551 # Collect counts for differential representation |
552 upCount[i] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh) | |
553 downCount[i] <- sum(top$table$FDR < fdrThresh & | |
554 top$table$logFC < -lfcThresh) | |
555 flatCount[i] <- sum(top$table$FDR > fdrThresh | | |
556 abs(top$table$logFC) < lfcThresh) | |
557 | |
2 | 558 # Make a plot of logFC versus logCPM |
559 png(smearPng[i], height=600, width=600) | |
560 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], | |
561 fixed=TRUE)) | |
562 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) | |
563 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) | |
564 | |
565 imgName <- paste0("Smear Plot(", contrastData[i], ")") | |
566 imgAddr <- paste0("smear(", contrastData[i], ").png") | |
567 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
568 invisible(dev.off()) | |
569 | |
570 pdf(smearPdf[i]) | |
571 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i], | |
572 fixed=TRUE)) | |
573 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle) | |
574 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2) | |
575 | |
576 linkName <- paste0("Smear Plot(", contrastData[i], ") (.pdf)") | |
577 linkAddr <- paste0("smear(", contrastData[i], ").pdf") | |
578 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
579 invisible(dev.off()) | |
580 | |
581 genes <- as.character(data$genes$Gene) | |
582 unq <- unique(genes) | |
583 unq <- unq[!is.na(unq)] | |
584 geneList <- list() | |
585 for (gene in unq) { | |
586 if (length(which(genes==gene)) >= hairpinReq) { | |
587 geneList[[gene]] <- which(genes==gene) | |
588 } | |
589 } | |
590 | |
591 if (wantRoast) { | |
592 # Input preparaton for roast | |
7 | 593 nrot <- 9999 |
2 | 594 set.seed(602214129) |
595 roastData <- mroast(data, index=geneList, design=design, | |
596 contrast=contrasts, nrot=nrot) | |
597 roastData <- cbind(GeneID=rownames(roastData), roastData) | |
598 write.table(roastData, file=roastOut[i], row.names=FALSE, sep="\t") | |
599 linkName <- paste0("Gene Level Analysis Table(", contrastData[i], | |
600 ") (.tsv)") | |
8 | 601 linkAddr <- paste0("gene_level(", contrastData[i], ").tsv") |
2 | 602 linkData <- rbind(linkData, c(linkName, linkAddr)) |
603 if (selectOpt=="rank") { | |
604 selectedGenes <- rownames(roastData)[selectVals] | |
605 } else { | |
606 selectedGenes <- selectVals | |
607 } | |
608 | |
609 if (packageVersion("limma")<"3.19.19") { | |
610 png(barcodePng[i], width=600, height=length(selectedGenes)*150) | |
611 } else { | |
612 png(barcodePng[i], width=600, height=length(selectedGenes)*300) | |
613 } | |
614 par(mfrow=c(length(selectedGenes), 1)) | |
615 for (gene in selectedGenes) { | |
616 barcodeplot(testData$table$logFC, index=geneList[[gene]], | |
617 main=paste("Barcode Plot for", gene, "(logFCs)", | |
618 gsub(".", " ", contrastData[i])), | |
619 labels=c("Positive logFC", "Negative logFC")) | |
620 } | |
621 imgName <- paste0("Barcode Plot(", contrastData[i], ")") | |
622 imgAddr <- paste0("barcode(", contrastData[i], ").png") | |
623 imageData <- rbind(imageData, c(imgName, imgAddr)) | |
624 dev.off() | |
625 if (packageVersion("limma")<"3.19.19") { | |
626 pdf(barcodePdf[i], width=8, height=2) | |
627 } else { | |
628 pdf(barcodePdf[i], width=8, height=4) | |
629 } | |
630 for (gene in selectedGenes) { | |
631 barcodeplot(testData$table$logFC, index=geneList[[gene]], | |
632 main=paste("Barcode Plot for", gene, "(logFCs)", | |
633 gsub(".", " ", contrastData[i])), | |
634 labels=c("Positive logFC", "Negative logFC")) | |
635 } | |
636 linkName <- paste0("Barcode Plot(", contrastData[i], ") (.pdf)") | |
637 linkAddr <- paste0("barcode(", contrastData[i], ").pdf") | |
638 linkData <- rbind(linkData, c(linkName, linkAddr)) | |
639 dev.off() | |
640 } | |
641 } | |
642 } | |
643 | |
8 | 644 # Generate data frame of the significant differences |
7 | 645 sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount) |
646 if (workMode == "glm") { | |
647 row.names(sigDiff) <- contrastData | |
648 } else if (workMode == "classic") { | |
649 row.names(sigDiff) <- paste0(pairData[2], "-", pairData[1]) | |
650 } | |
651 | |
8 | 652 # Output table of summarised counts |
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653 ID <- rownames(data$counts) |
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654 outputCounts <- cbind(ID, data$counts) |
8 | 655 write.table(outputCounts, file=countsOut, row.names=FALSE, sep="\t", |
656 quote=FALSE) | |
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657 linkName <- "Counts table (.tsv)" |
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658 linkAddr <- "counts.tsv" |
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659 linkData <- rbind(linkData, c(linkName, linkAddr)) |
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660 |
8 | 661 # Record session info |
662 writeLines(capture.output(sessionInfo()), sessionOut) | |
663 linkData <- rbind(linkData, c("Session Info", "session_info.txt")) | |
664 | |
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665 # Record ending time and calculate total run time |
2 | 666 timeEnd <- as.character(Sys.time()) |
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667 timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3)) |
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668 timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE) |
2 | 669 ################################################################################ |
670 ### HTML Generation | |
671 ################################################################################ | |
672 # Clear file | |
673 cat("", file=htmlPath) | |
674 | |
675 cata("<html>\n") | |
676 HtmlHead("EdgeR Output") | |
677 | |
678 cata("<body>\n") | |
679 cata("<h3>EdgeR Analysis Output:</h3>\n") | |
680 cata("<h4>Input Summary:</h4>\n") | |
681 if (inputType=="fastq") { | |
682 cata("<ul>\n") | |
683 ListItem(hpReadout[1]) | |
684 ListItem(hpReadout[2]) | |
685 cata("</ul>\n") | |
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686 cata(hpReadout[3], "<br />\n") |
2 | 687 cata("<ul>\n") |
688 ListItem(hpReadout[4]) | |
689 ListItem(hpReadout[7]) | |
690 cata("</ul>\n") | |
6
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diff
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691 cata(hpReadout[8:11], sep="<br />\n") |
2 | 692 cata("<br />\n") |
693 cata("<b>Please check that read percentages are consistent with ") | |
694 cata("expectations.</b><br >\n") | |
695 } else if (inputType=="counts") { | |
696 cata("<ul>\n") | |
697 ListItem("Number of Samples: ", ncol(data$counts)) | |
698 ListItem("Number of Hairpins: ", countsRows) | |
699 ListItem("Number of annotations provided: ", annoRows) | |
700 ListItem("Number of annotations matched to hairpin: ", annoMatched) | |
701 cata("</ul>\n") | |
702 } | |
703 | |
704 cata("The estimated common biological coefficient of variation (BCV) is: ", | |
705 commonBCV, "<br />\n") | |
706 | |
707 cata("<h4>Output:</h4>\n") | |
7 | 708 cata("PDF copies of JPEGS available in 'Plots' section.<br />\n") |
2 | 709 for (i in 1:nrow(imageData)) { |
710 if (grepl("barcode", imageData$Link[i])) { | |
711 if (packageVersion("limma")<"3.19.19") { | |
712 HtmlImage(imageData$Link[i], imageData$Label[i], | |
713 height=length(selectedGenes)*150) | |
714 } else { | |
715 HtmlImage(imageData$Link[i], imageData$Label[i], | |
716 height=length(selectedGenes)*300) | |
717 } | |
718 } else { | |
719 HtmlImage(imageData$Link[i], imageData$Label[i]) | |
720 } | |
721 } | |
6
3d04308a99f9
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722 cata("<br />\n") |
2 | 723 |
7 | 724 cata("<h4>Differential Representation Counts:</h4>\n") |
725 | |
726 cata("<table border=\"1\" cellpadding=\"4\">\n") | |
727 cata("<tr>\n") | |
728 TableItem() | |
729 for (i in colnames(sigDiff)) { | |
730 TableHeadItem(i) | |
731 } | |
732 cata("</tr>\n") | |
733 for (i in 1:nrow(sigDiff)) { | |
734 cata("<tr>\n") | |
735 TableHeadItem(unmake.names(row.names(sigDiff)[i])) | |
736 for (j in 1:ncol(sigDiff)) { | |
737 TableItem(as.character(sigDiff[i, j])) | |
738 } | |
739 cata("</tr>\n") | |
740 } | |
741 cata("</table>") | |
742 | |
2 | 743 cata("<h4>Plots:</h4>\n") |
744 for (i in 1:nrow(linkData)) { | |
8 | 745 if (grepl(".pdf", linkData$Link[i])) { |
2 | 746 HtmlLink(linkData$Link[i], linkData$Label[i]) |
747 } | |
748 } | |
749 | |
750 cata("<h4>Tables:</h4>\n") | |
751 for (i in 1:nrow(linkData)) { | |
752 if (grepl(".tsv", linkData$Link[i])) { | |
753 HtmlLink(linkData$Link[i], linkData$Label[i]) | |
754 } | |
755 } | |
756 | |
7 | 757 cata("<p>Alt-click links to download file.</p>\n") |
758 cata("<p>Click floppy disc icon associated history item to download ") | |
759 cata("all files.</p>\n") | |
760 cata("<p>.tsv files can be viewed in Excel or any spreadsheet program.</p>\n") | |
6
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761 |
3d04308a99f9
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762 cata("<h4>Additional Information:</h4>\n") |
3d04308a99f9
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diff
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763 |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
764 if (inputType == "fastq") { |
3d04308a99f9
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765 ListItem("Data was gathered from fastq raw read file(s).") |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
766 } else if (inputType == "counts") { |
3d04308a99f9
- Added differentially expressed hairpin count output
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parents:
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diff
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767 ListItem("Data was gathered from a table of counts.") |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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768 } |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
769 |
3d04308a99f9
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770 if (cpmReq!=0 && sampleReq!=0) { |
8 | 771 tempStr <- paste("Hairpins without more than", cpmReq, |
7 | 772 "CPM in at least", sampleReq, "samples are insignificant", |
773 "and filtered out.") | |
6
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diff
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774 ListItem(tempStr) |
3d04308a99f9
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diff
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|
775 filterProp <- round(filteredCount/preFilterCount*100, digits=2) |
3d04308a99f9
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776 tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp, |
3d04308a99f9
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777 "%) hairpins were filtered out for low count-per-million.") |
3d04308a99f9
- Added differentially expressed hairpin count output
shian_su <registertonysu@gmail.com>
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diff
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|
778 ListItem(tempStr) |
3d04308a99f9
- Added differentially expressed hairpin count output
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4
diff
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|
779 } |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
780 |
3d04308a99f9
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diff
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781 if (workMode == "classic") { |
3d04308a99f9
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782 ListItem("An exact test was performed on each hairpin.") |
3d04308a99f9
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|
783 } else if (workMode == "glm") { |
3d04308a99f9
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|
784 ListItem("A generalised linear model was fitted to each hairpin.") |
3d04308a99f9
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parents:
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diff
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785 } |
3d04308a99f9
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diff
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|
786 |
3d04308a99f9
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4
diff
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|
787 cit <- character() |
3d04308a99f9
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diff
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|
788 link <-character() |
3d04308a99f9
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789 link[1] <- paste0("<a href=\"", |
3d04308a99f9
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790 "http://www.bioconductor.org/packages/release/bioc/", |
3d04308a99f9
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|
791 "vignettes/limma/inst/doc/usersguide.pdf", |
3d04308a99f9
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|
792 "\">", "limma User's Guide", "</a>.") |
3d04308a99f9
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793 link[2] <- paste0("<a href=\"", |
3d04308a99f9
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794 "http://www.bioconductor.org/packages/release/bioc/", |
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795 "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf", |
3d04308a99f9
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796 "\">", "edgeR User's Guide", "</a>") |
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797 |
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798 cit[1] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010).", |
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|
799 "edgeR: a Bioconductor package for differential", |
3d04308a99f9
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800 "expression analysis of digital gene expression", |
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diff
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|
801 "data. Bioinformatics 26, 139-140") |
3d04308a99f9
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802 cit[2] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests", |
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diff
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803 "for assessing differences in tag abundance. Bioinformatics", |
3d04308a99f9
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diff
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|
804 "23, 2881-2887") |
3d04308a99f9
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805 cit[3] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of", |
3d04308a99f9
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diff
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806 "negative binomial dispersion, with applications to SAGE data.", |
3d04308a99f9
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diff
changeset
|
807 "Biostatistics, 9, 321-332") |
3d04308a99f9
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diff
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|
808 |
3d04308a99f9
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|
809 cit[4] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential", |
3d04308a99f9
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|
810 "expression analysis of multifactor RNA-Seq experiments with", |
3d04308a99f9
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diff
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|
811 "respect to biological variation. Nucleic Acids Research 40,", |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
changeset
|
812 "4288-4297") |
3d04308a99f9
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4
diff
changeset
|
813 |
3d04308a99f9
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diff
changeset
|
814 cata("<h4>Citations</h4>") |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
815 cata("<ol>\n") |
3d04308a99f9
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diff
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|
816 ListItem(cit[1]) |
3d04308a99f9
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4
diff
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|
817 ListItem(cit[2]) |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
818 ListItem(cit[3]) |
3d04308a99f9
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diff
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|
819 ListItem(cit[4]) |
3d04308a99f9
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diff
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|
820 cata("</ol>\n") |
3d04308a99f9
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parents:
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diff
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|
821 |
8 | 822 cata("<p>Report problems to: su.s@wehi.edu.au</p>\n") |
823 | |
824 for (i in 1:nrow(linkData)) { | |
825 if (grepl("session_info", linkData$Link[i])) { | |
826 HtmlLink(linkData$Link[i], linkData$Label[i]) | |
827 } | |
828 } | |
829 | |
2 | 830 cata("<table border=\"0\">\n") |
831 cata("<tr>\n") | |
832 TableItem("Task started at:"); TableItem(timeStart) | |
833 cata("</tr>\n") | |
834 cata("<tr>\n") | |
835 TableItem("Task ended at:"); TableItem(timeEnd) | |
836 cata("</tr>\n") | |
6
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
changeset
|
837 cata("<tr>\n") |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
changeset
|
838 TableItem("Task run time:"); TableItem(timeTaken) |
3d04308a99f9
- Added differentially expressed hairpin count output
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diff
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|
839 cata("<tr>\n") |
3d04308a99f9
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diff
changeset
|
840 cata("</table>\n") |
2 | 841 |
842 cata("</body>\n") | |
843 cata("</html>") |