comparison pipe-t.R @ 2:6cd22b1fbf6d draft

planemo upload for repository https://github.com/igg-molecular-biology-lab/pipe-t.git commit 18d030f0c2e423a04617a3827ba5a652c8d7867a

1:ecd0a79e8130

          }
      }
  }
cat("\n Initialization completed! \n")

readCtDataDav<-
function (files, path = NULL, n.features = 384, format = "plain",
    column.info, flag, feature, type, position, Ct, header = FALSE,
    SDS = FALSE, n.data = 1, samples, na.value = 40, sample.info,
    ...)

    out <- new("qPCRset", exprs = X, phenoData = sample.info,
        featureData = featData, featureCategory = X.cat, flag = X.flags,
        CtHistory = X.hist)
    out
}
.readCtEDS	<-
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Scan through beginning of file, max 100 lines
	file.header <- readLines(con=readfile, n=100)
	n.header	<- grep("^Well", file.header)
	if (length(n.header)==0)
		n.header	<- 0
	# Read data, skip the required lines
	out	<- read.delim(file=readfile, header=TRUE, colClasses="character", nrows=nspots*n.data[i], skip=n.header-1, strip.white=TRUE, ...)
	out
} # .readCtEDS

head(read.delim(file.path(path000, dpfiles), sep="\t"))
files <- read.delim(file.path(path000, dpfiles), sep="\t")
switch(format,
  "EDS"={

		setCtHistory(q)	<- data.frame(history="Manually created qPCRset object.", stringsAsFactors=FALSE)
	setCtHistory(q)	<- rbind(getCtHistory(q), capture.output(match.call(normalizeCtData)))
	# Return the normalised object
	q
}


switch(normalizationMethod,
    "deltaCt"={
      normalizedDataset <- normalizeCtDataDav(xFilter, norm="deltaCt",  deltaCt.genes =explode(normalizers, sep = ","))
    },

2:6cd22b1fbf6d

          }
      }
  }
cat("\n Initialization completed! \n")

.readCtEDS	<-
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Scan through beginning of file, max 100 lines
	file.header <- readLines(con=readfile, n=100)
	n.header	<- grep("^Well", file.header)
	if (length(n.header)==0)
		n.header	<- 0
	# Read data, skip the required lines
	out	<- read.delim(file=readfile, header=TRUE, colClasses="character", nrows=nspots*n.data[i], skip=n.header-1, strip.white=TRUE, ...)
	out
} # .readCtEDS


.readCtPlain	<- 
function(readfile=readfile, header=header, n.features=n.features, n.data=n.data, i=i, ...)
{
	# A check for file dimensions. Read a single file.
	sample	<- read.delim(file=readfile, header=header, ...)
	nspots	<- nrow(sample)
	if (nspots != n.features*n.data[1])
		warning(paste(n.features, "gene names (rows) expected, got", nspots))
	# Read in the required file
	out	<- read.delim(file=readfile, header=header, colClasses="character", nrows=nspots*n.data[i], ...)
	# Return
	out
} # .readCtPlain

.readCtSDS	<- 
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Scan through beginning of file, max 100 lines
	file.header <- readLines(con=readfile, n=100)
	n.header	<- grep("^#", file.header)
	if (length(n.header)==0) 
		n.header	<- 0
	# Read data, skip the required lines
	out	<- read.delim(file=readfile, header=FALSE, colClasses="character", nrows=nspots*n.data[i], skip=n.header, strip.white=TRUE, ...)
	# Return
	out
} # .readCtSDS

.readCtLightCycler	<- 
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Read data, skip the required lines
	out	<- read.delim(file=readfile, header=TRUE, as.is=TRUE, nrows=nspots*n.data[i], skip=1, strip.white=TRUE, ...)
	# Return
	out
} # .readCtLightCycler

.readCtCFX	<- function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Read data, skip the required lines
	out	<- read.csv(file=readfile, header=TRUE, as.is=TRUE, nrows=nspots*n.data[i], strip.white=TRUE, ...)
	# Return
	out
} # .readCtCFX

.readCtOpenArray	<- 
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Read data
	out	<- read.csv(file=readfile, header=TRUE, as.is=TRUE, nrows=nspots*n.data[i], strip.white=TRUE, ...)
	# Regard those marked as outliers as "Unreliable"
	out$ThroughHole.Outlier[out$ThroughHole.Outlier=="False"]	<- "OK"
	out$ThroughHole.Outlier[out$ThroughHole.Outlier=="True"]	<- "Unreliable"	
	# Return
	out
} # .readCtOpenArray

.readCtBioMark	<- 
function(readfile=readfile, n.data=n.data, i=i, nspots=nspots, ...)
{
	# Scan through beginning of file, max 100 lines
	file.header <- readLines(con=readfile, n=100)
	n.header	<- grep("^ID", file.header)-1
	if (length(n.header)==0) 
		n.header	<- 0
	# Read data, skip the required lines
	out	<- read.csv(file=readfile, header=TRUE, as.is=TRUE, nrows=nspots*n.data[i], skip=n.header, strip.white=TRUE, ...)
	# Convert the calls into flags
	out$Call[out$Call=="Pass"] <- "OK"
	out$Call[out$Call=="Fail"] <- "Undetermined"	
	# Return
	out
} # .readCtBioMark



readCtDataDav<-
function (files, path = NULL, n.features = 384, format = "plain",
    column.info, flag, feature, type, position, Ct, header = FALSE,
    SDS = FALSE, n.data = 1, samples, na.value = 40, sample.info,
    ...)

    out <- new("qPCRset", exprs = X, phenoData = sample.info,
        featureData = featData, featureCategory = X.cat, flag = X.flags,
        CtHistory = X.hist)
    out
}


head(read.delim(file.path(path000, dpfiles), sep="\t"))
files <- read.delim(file.path(path000, dpfiles), sep="\t")
switch(format,
  "EDS"={

		setCtHistory(q)	<- data.frame(history="Manually created qPCRset object.", stringsAsFactors=FALSE)
	setCtHistory(q)	<- rbind(getCtHistory(q), capture.output(match.call(normalizeCtData)))
	# Return the normalised object
	q
}
#library(NormqPCR)

#delete.na <- function(DF, n=0) {
 # DF[rowSums(is.na(DF)) <= n,]
#}

#user_number=5
#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)
#normfinder <- selectHKs(as.matrix(t(delete.na(exprs(xGlico),0))), group= files$Treatment , method = "NormFinder", Symbols =rownames(as.matrix(delete.na(exprs(xGlico),0))), minNrHK = as.numeric(user_number), log = TRUE)
#intersection= intersect(normfinder$ranking, genorm$ranking[1:as.numeric(user_number)])

#cat("\n GeNorm and NormFinder transcripts selected as housekeeping for normalization! \n")
#intersection
#dnorm <- normalizeCtData(xGlico , norm="deltaCt", deltaCt.genes=as.vector(intersection)) 

switch(normalizationMethod,
    "deltaCt"={
      normalizedDataset <- normalizeCtDataDav(xFilter, norm="deltaCt",  deltaCt.genes =explode(normalizers, sep = ","))
    },