comparison pipe-t.R @ 15:5e8bf316343d draft

planemo upload for repository https://github.com/igg-molecular-biology-lab/pipe-t.git commit d5c46b42061ff823c19437d1c803119ef8b95627

14:3168db2e0ff5

	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	<- 

        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.")
    }
    if (missing(column.info)) {
        column.info <- switch(format, EDS = list(flag="EXPFAIL", feature="Target.Name",  position="Well.Position", Ct="CT"),
          plain = list(flag = 4, feature = 6, type = 7, position = 3, Ct = 8), 
            SDS = list(flag = 4,feature = 6, type = 7, position = 3, Ct = 8), 
            #SDS = list(flag = "Omit",feature = "Detector", type = "Task", position = "Pos", Ct = "Avg.Ct"), 
            LightCycler = list(feature = "Name",
            position = "Pos", Ct = "Cp"), CFX = list(feature = "Content",
            position = "Well", Ct = "Cq.Mean"), OpenArray = list(flag = "ThroughHole.Outlier",
            feature = "Assay.Assay.ID", type = "Assay.Assay.Type",
            position = "ThroughHole.Address", Ct = "ThroughHole.Ct"),

            data[data %in% nas | data == 0] <- NA
        }
        else {
            data[data %in% nas | is.na(data) | data == 0] <- na.value
        }
        X[, cols] <- apply(data, 2, function(x) as.numeric(as.character(x)))
        if ("flag" %in% names(column.info)) {
            flags <- matrix(sample[, column.info[["flag"]]],
                ncol = n.data[i])
            flags[flags == "-"] <- "Failed"
            flags[flags == "+"] <- "Passed"

      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)
      raw<- readCtDataDav(files = as.vector(files$sampleName), header=FALSE,  format="plain", path = path, sample.info=phenoData,n.features = as.numeric(nfeatures))
    },
    "SDS"={
      columns<- list(feature=3, Ct=6, flag=11)
      #columns <-list(flag = "Omit",feature = "Detector", type = "Task", position = "Wells", Ct = "Avg.Ct")
      metadata <- data.frame(labelDescription = c("sampleName", "Treatment"),  row.names = c("sampleName", "Treatment"))
      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)
      raw<- readCtDataDav(files = files$sampleName, format="SDS",column.info=columns, path = path, sample.info=phenoData, n.features=as.numeric(nfeatures))
    },
    "LightCycler"={
      metadata <- data.frame(labelDescription = c("sampleName", "Treatment"),  row.names = c("sampleName", "Treatment"))
      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)

15:5e8bf316343d

	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=TRUE, colClasses="character", nrows=nspots*n.data[i], skip=n.header-1, strip.white=TRUE, ...)
	# Return
	out
} # .readCtSDS

.readCtLightCycler	<- 

        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.")
    }
    if (missing(column.info)) {
        column.info <- switch(format, EDS = list(flag="EXPFAIL", feature="Target.Name",  position="Well.Position", Ct="CT"),
          plain = list(flag = 4, feature = 6, type = 7, position = 3, Ct = 8), 
            #SDS = list(flag = 4,feature = 6, type = 7, position = 3, Ct = 8), 
            SDS = list(flag = "Omit",feature = "Detector", type = "Task", position = "Pos", Ct = "Avg.Ct"), 
            LightCycler = list(feature = "Name",
            position = "Pos", Ct = "Cp"), CFX = list(feature = "Content",
            position = "Well", Ct = "Cq.Mean"), OpenArray = list(flag = "ThroughHole.Outlier",
            feature = "Assay.Assay.ID", type = "Assay.Assay.Type",
            position = "ThroughHole.Address", Ct = "ThroughHole.Ct"),

            data[data %in% nas | data == 0] <- NA
        }
        else {
            data[data %in% nas | is.na(data) | data == 0] <- na.value
        }
        X[, cols] <- suppressWarnings(apply(data, 2, function(x) as.numeric(as.character(x))))
        if ("flag" %in% names(column.info)) {
            flags <- matrix(sample[, column.info[["flag"]]],
                ncol = n.data[i])
            flags[flags == "-"] <- "Failed"
            flags[flags == "+"] <- "Passed"

      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)
      raw<- readCtDataDav(files = as.vector(files$sampleName), header=FALSE,  format="plain", path = path, sample.info=phenoData,n.features = as.numeric(nfeatures))
    },
    "SDS"={
      #columns<- list(feature=3, Ct=6, flag=11)
      #columns <-list(flag = "Omit",feature = "Detector", type = "Task", position = "Wells", Ct = "Avg.Ct")
      metadata <- data.frame(labelDescription = c("sampleName", "Treatment"),  row.names = c("sampleName", "Treatment"))
      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)
      raw<- readCtDataDav(files = files$sampleName, header=TRUE,format="SDS",path = path, sample.info=phenoData, n.features=as.numeric(nfeatures))
    },
    "LightCycler"={
      metadata <- data.frame(labelDescription = c("sampleName", "Treatment"),  row.names = c("sampleName", "Treatment"))
      phenoData <- new("AnnotatedDataFrame", data = files, varMetadata = metadata)
      rownames(phenoData)=as.vector(files$sampleName)