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comparison pipe-t.R @ 7:3e099c082954 draft

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planemo upload for repository https://github.com/igg-molecular-biology-lab/pipe-t.git commit 5c55ecdfd6ed19c7eb7558f278884511620df5dd
author davidecangelosi
date Tue, 07 May 2019 06:03:18 -0400
parents d1cabb2bc795
children e5953805ad7a
comparison
equal deleted inserted replaced
6:d1cabb2bc795 7:3e099c082954
175 file.header <- readLines(con=readfile, n=100) 175 file.header <- readLines(con=readfile, n=100)
176 n.header <- grep("^#", file.header) 176 n.header <- grep("^#", file.header)
177 if (length(n.header)==0) 177 if (length(n.header)==0)
178 n.header <- 0 178 n.header <- 0
179 # Read data, skip the required lines 179 # Read data, skip the required lines
180 out <- read.delim(file=readfile, header=TRUE, colClasses="character", nrows=nspots*n.data[i], skip=n.header-1, strip.white=TRUE, ...) 180 out <- read.delim(file=readfile, header=FALSE, colClasses="character", nrows=nspots*n.data[i], skip=n.header, strip.white=TRUE, ...)
181 # Return 181 # Return
182 out 182 out
183 } # .readCtSDS 183 } # .readCtSDS
184 184
185 .readCtLightCycler <- 185 .readCtLightCycler <-
255 warning("Please use 'column.info' for providing a list of column numbers containing particular information. The use of 'flag', 'feature', 'type', 'position' and 'Ct' is deprecated and will be removed in future versions.") 255 warning("Please use 'column.info' for providing a list of column numbers containing particular information. The use of 'flag', 'feature', 'type', 'position' and 'Ct' is deprecated and will be removed in future versions.")
256 } 256 }
257 if (missing(column.info)) { 257 if (missing(column.info)) {
258 column.info <- switch(format, EDS = list(flag="EXPFAIL", feature="Target.Name", position="Well.Position", Ct="CT"), 258 column.info <- switch(format, EDS = list(flag="EXPFAIL", feature="Target.Name", position="Well.Position", Ct="CT"),
259 plain = list(flag = 4, feature = 6, type = 7, position = 3, Ct = 8), 259 plain = list(flag = 4, feature = 6, type = 7, position = 3, Ct = 8),
260 #SDS = list(flag = 4,feature = 6, type = 7, position = 3, Ct = 8), 260 SDS = list(flag = 4,feature = 6, type = 7, position = 3, Ct = 8),
261 SDS = list(flag = "Omit",feature = "Detector", type = "Task", position = "Wells", Ct = "Ct"), 261 #SDS = list(flag = "Omit",feature = "Detector", type = "Task", position = "Pos", Ct = "Avg.Ct"),
262 LightCycler = list(feature = "Name", 262 LightCycler = list(feature = "Name",
263 position = "Pos", Ct = "Cp"), CFX = list(feature = "Content", 263 position = "Pos", Ct = "Cp"), CFX = list(feature = "Content",
264 position = "Well", Ct = "Cq.Mean"), OpenArray = list(flag = "ThroughHole.Outlier", 264 position = "Well", Ct = "Cq.Mean"), OpenArray = list(flag = "ThroughHole.Outlier",
265 feature = "Assay.Assay.ID", type = "Assay.Assay.Type", 265 feature = "Assay.Assay.ID", type = "Assay.Assay.Type",
266 position = "ThroughHole.Address", Ct = "ThroughHole.Ct"), 266 position = "ThroughHole.Address", Ct = "ThroughHole.Ct"),
287 n.data = n.data, i = i, nspots = nspots, ...), LightCycler = .readCtLightCycler(readfile = readfile, 287 n.data = n.data, i = i, nspots = nspots, ...), LightCycler = .readCtLightCycler(readfile = readfile,
288 n.data = n.data, i = i, nspots = nspots, ...), CFX = .readCtCFX(readfile = readfile, 288 n.data = n.data, i = i, nspots = nspots, ...), CFX = .readCtCFX(readfile = readfile,
289 n.data = n.data, i = i, nspots = nspots, ...), OpenArray = .readCtOpenArray(readfile = readfile, 289 n.data = n.data, i = i, nspots = nspots, ...), OpenArray = .readCtOpenArray(readfile = readfile,
290 n.data = n.data, i = i, nspots = nspots, ...), BioMark = .readCtBioMark(readfile = readfile, 290 n.data = n.data, i = i, nspots = nspots, ...), BioMark = .readCtBioMark(readfile = readfile,
291 n.data = n.data, i = i, nspots = nspots, ...)) 291 n.data = n.data, i = i, nspots = nspots, ...))
292 292 #if (format == "SDS") {
293 data <- matrix(sample[, column.info[["Ct"]]], ncol = n.data[i]) 293 # if("Avg Ct" %in% colnames(n.data)){
294 # data <- matrix(sample[, column.info[["Avg.Ct"]]], ncol = n.data[i])
295 # } elseif {
296 # cat("\n Unsupported SDS format! ")
297 # }
298 #}else{
299 data <- matrix(sample[, column.info[["Ct"]]], ncol = n.data[i])
300 # }
294 undeter <- apply(data, 2, function(x) x %in% c("Undetermined", 301 undeter <- apply(data, 2, function(x) x %in% c("Undetermined",
295 "No Ct")) 302 "No Ct"))
296 X.cat[, cols][undeter] <- "Undetermined" 303 X.cat[, cols][undeter] <- "Undetermined"
297 nas <- c("Undetermined", "No Ct", "999", "N/A") 304 nas <- c("Undetermined", "No Ct", "999", "N/A")
298 if (is.null(na.value)) { 305 if (is.null(na.value)) {
392 rownames(phenoData)=as.vector(files$sampleName) 399 rownames(phenoData)=as.vector(files$sampleName)
393 raw<- readCtDataDav(files = as.vector(files$sampleName), header=FALSE, format="plain", path = path, sample.info=phenoData,n.features = as.numeric(nfeatures)) 400 raw<- readCtDataDav(files = as.vector(files$sampleName), header=FALSE, format="plain", path = path, sample.info=phenoData,n.features = as.numeric(nfeatures))
394 }, 401 },
395 "SDS"={ 402 "SDS"={
396 #columns<- list(feature=3, Ct=6, flag=11) 403 #columns<- list(feature=3, Ct=6, flag=11)
397 columns <-list(flag = "Omit",feature = "Detector", type = "Task", position = "Wells", Ct = "Ct") 404 columns <-list(flag = "Omit",feature = "Detector", type = "Task", position = "Wells", Ct = "Avg.Ct")
398 metadata <- data.frame(labelDescription = c("sampleName", "Treatment"), row.names = c("sampleName", "Treatment")) 405 metadata <- data.frame(labelDescription = c("sampleName", "Treatment"), row.names = c("sampleName", "Treatment"))
399 phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata) 406 phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
400 rownames(phenoData)=as.vector(files$sampleName) 407 rownames(phenoData)=as.vector(files$sampleName)
401 raw<- readCtDataDav(files = files$sampleName, format="SDS",column.info=columns, path = path, sample.info=phenoData, n.features=as.numeric(nfeatures)) 408 raw<- readCtDataDav(files = files$sampleName, format="SDS",column.info=columns, path = path, sample.info=phenoData, n.features=as.numeric(nfeatures))
402 }, 409 },
629 # DF[rowSums(is.na(DF)) <= n,] 636 # DF[rowSums(is.na(DF)) <= n,]
630 #} 637 #}
631 638
632 #user_number=5 639 #user_number=5
633 #genorm <- selectHKs(t(delete.na(as.matrix(exprs(xGlico)),0)), method = "geNorm", Symbols = rownames(as.matrix(delete.na(exprs(xGlico),0))), minNrHK = as.numeric(user_number), log = TRUE) 640 #genorm <- selectHKs(t(delete.na(as.matrix(exprs(xGlico)),0)), method = "geNorm", Symbols = rownames(as.matrix(delete.na(exprs(xGlico),0))), minNrHK = as.numeric(user_number), log = TRUE)
641 #genorm
634 #normfinder <- selectHKs(as.matrix(t(delete.na(exprs(xGlico),0))), group= files$Treatment , method = "NormFinder", Symbols =rownames(as.matrix(delete.na(exprs(xGlico),0))), minNrHK = as.numeric(user_number), log = TRUE) 642 #normfinder <- selectHKs(as.matrix(t(delete.na(exprs(xGlico),0))), group= files$Treatment , method = "NormFinder", Symbols =rownames(as.matrix(delete.na(exprs(xGlico),0))), minNrHK = as.numeric(user_number), log = TRUE)
643 #normfinder
635 #intersection= intersect(normfinder$ranking, genorm$ranking[1:as.numeric(user_number)]) 644 #intersection= intersect(normfinder$ranking, genorm$ranking[1:as.numeric(user_number)])
636 645
637 #cat("\n GeNorm and NormFinder transcripts selected as housekeeping for normalization! \n") 646 #cat("\n GeNorm and NormFinder transcripts selected as housekeeping for normalization! \n")
638 #intersection 647 #intersection
639 #dnorm <- normalizeCtData(xGlico , norm="deltaCt", deltaCt.genes=as.vector(intersection)) 648 #dnorm <- normalizeCtData(xGlico , norm="deltaCt", deltaCt.genes=as.vector(intersection))