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R/openData_funct_lspfp.R
In LSPFP: Lysate and Secretome Peptide Feature Plotter
# Secretome Lysat Peptide Descriptor
#
# sourcefile functions
#--------------------------------------------------------------------------------------
# open txt file of an experiment fitted to the newest MaxQuant output
# Input: list experiment, numeric vector fileNumber indicating the fileName stored in experiment$FileName
# Output: list of data.table data, list experiment
openTXT <- function (experiment, fileNumber)
{
txt <- "- Starting TXT-read..."
#printTcl(txt)
print(txt)
expNamesTmp <- ""
data <- {}
fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
"Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id",
"Carbamidomethyl (C) site IDs","NEM site IDs")
header <- readLines(fileName,n=1)
header2 <- unlist(strsplit(header,"\t"))
data <- suppressWarnings(data.table::fread(fileName,
colClasses=list(character=characterCols[characterCols %in% header2]),
integer64 = "double"))
# replace all spaces
setnames(data, gsub(" ", "_", colnames(data)))
setnames(data, "Leading_razor_protein", "Accession")
setnames(data, "Unique_(Groups)", "Unique")
setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Delete rows with Reverse or Contaminat = "+"
#data <- data[Reverse != "+"]
#data <- data[Potential_contaminant != "+"]
# try to avoid warning
data <- data["Reverse" != "+"]
data <- data["Potential_contaminant" != "+"]
# Include index in table
#data[,dataIndex:=(1:nrow(data))]
#try to avoid warning
data[,"dataIndex":=(1:nrow(data))]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
# Change column name for the Accession numbers
# setnames(data, "Leading razor protein", "Accession")
# setnames(data, "Unique (Groups)", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
# set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
# set(data,which(data[,Unique]=="no"),"Unique","FALSE")
#try to avoid warning
#print(bla)
Unique <- NULL
set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
set(data,which(data[,Unique]=="no"),"Unique","FALSE")
data$Unique <- as.logical(data$Unique)
# setnames(data, gsub(" ", "_", colnames(data)))
# setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Extracting the number of different experimental groups.
# The 1st data row it is checked for "Normalized abundance" and "Spectral counts".
# The 2nd row is checked for the number and the names of experimental groups.
expColsInt <- which(substring((colnames(data)),1, 10) == "Intensity_")
experiment$StartColIntensities[fileNumber] <- head(expColsInt, 1)
expColsCnt <- which(substring((colnames(data)),1, 7) == "Counts_")
experiment$StartColCounts[fileNumber] <- head(expColsCnt, 1)
experiment$NumExps[fileNumber] <- length(expColsInt)
if (length(length(expColsInt)) != length(length(expColsCnt)))
{
txt <- "- Warning: Experiment and Intensity numbers do not match ! ! !"
#tkinsert(txtWidget,"0.0",paste(txt, "\n"))
print(txt)
}
experiment$ExpNames[fileNumber] <- list(substring(colnames(data)[expColsInt], 11))
# suppressWarnings(
# for (j in names(data[,startColCounts[fileNumber]:(startColCounts[fileNumber]+(numExps[fileNumber]*2)-1), with=FALSE])){
# set(data,which(is.na(data[[j]])),j,0)
# }
# )
setnames(data, paste0(colnames(data), "_File", fileNumber))
experiment$ColumnNamesInt[fileNumber] <- list(colnames(data)[expColsInt])
experiment$ColumnNamesCnt[fileNumber] <- list(colnames(data)[expColsCnt])
return(list(data, experiment))
}
#--------------------------------------------
# open txt file of an experiment fittet to the newest MaxQuant output
# Input: path to peptide.txt
# Output: data.table data
ss_openTXT <- function (path)
{
txt <- "- Starting TXT-read..."
print(txt)
expNamesTmp <- ""
data <- {}
#fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
#characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
# "Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id")
characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
"Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id",
"Carbamidomethyl (C) site IDs","NEM site IDs")
header <- readLines(path,n=1)
header2 <- unlist(strsplit(header,"\t"))
data <- suppressWarnings(data.table::fread(path,
colClasses=list(character=characterCols[characterCols %in% header2]),
integer64 = "double"))
# data <- data.table::fread(path,
# colClasses=list(character=characterCols),
# integer64 = "double",verbose=TRUE)
# replace all spaces
setnames(data, gsub(" ", "_", colnames(data)))
#nnames <- gsub(" ","_", colnames(data))
# nnames(grep("Unique_\\(Groups\\)",nnames)) <- Unique
# nnames(grep("Leading_razor_protein",nnames)) <- Unique
setnames(data, "Leading_razor_protein", "Accession")
setnames(data, "Unique_(Groups)", "Unique")
setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Delete rows with Reverse or Contaminat = "+"
# data <- data[Reverse != "+"]
# data <- data[Potential_contaminant != "+"]
#try to avoid warning
data <- data["Reverse" != "+"]
data <- data["Potential_contaminant" != "+"]
# Include index in table
#data[,dataIndex:=(1:nrow(data))]
# try to avoid warnings
data[,"dataIndex":=(1:nrow(data))]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
# Change column name for the Accession numbers
# setnames(data, "Leading razor protein", "Accession")
# setnames(data, "Unique (Groups)", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
#set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
#set(data,which(data[,Unique]=="no"),"Unique","FALSE")
#try to avoid warnings
#print(bla)
Unique <- NULL
set(data,which(data[, Unique]=="yes"),"Unique","TRUE")
set(data,which(data[, Unique]=="no"),"Unique","FALSE")
data$Unique <- as.logical(data$Unique)
#setattr(data, "Unique", logical)
#data[, "Unique":=as.logical("Unique")]
# setnames(data, gsub(" ", "_", colnames(data)))
# setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Extracting the number of different experimental groups.
# The 1st data row it is checked for "Normalized abundance" and "Spectral counts".
# The 2nd row is checked for the number and the names of experimental groups.
expColsInt <- which(substring((colnames(data)),1, 10) == "Intensity_")
#experiment$StartColIntensities[fileNumber] <- head(expColsInt, 1)
expColsCnt <- which(substring((colnames(data)),1, 7) == "Counts_")
#experiment$StartColCounts[fileNumber] <- head(expColsCnt, 1)
#experiment$NumExps[fileNumber] <- length(expColsInt)
if (length(length(expColsInt)) != length(length(expColsCnt)))
{
txt <- "- Warning: Experiment and Intensity numbers do not match ! ! !"
print(txt)
}
return(data)
}
# open csv file of an experiment
# Input: list experiment, numeric vector fileNumber indicating the fileName stored in experiment$FileName
# Output: list of logical vector result, data.table data, list experiment
openCSV <- function (experiment, fileNumber)
{
data <- {}
result <- FALSE
txt <- "- Starting CSV-read..."
txt <- paste(txt, experiment$FileName[fileNumber])
print(txt)
fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
headers <- read.table(file = fileName, check.names = FALSE, sep = ',', header = FALSE, stringsAsFactors = FALSE, nrows = 3)
# warum wird hier die letzte Spalte abgeschnitten? da stehen Daten drin!
# fuehrt weiter unten auch zu Folgefehlern
# headers <- headers[-ncolHeaders]
# ncolHeaders <- ncol(headers)
experiment$StartColIntensities[fileNumber] <- which(headers[1,]=="Normalized abundance")
#test if there are Raw abundance values
until <- which(headers[1,]=="Spectral counts")-1
raw <- 0
if(sum(headers[1,]=="Raw abundance", na.rm = TRUE) >= 1)
{
raw <- which(headers[1,]=="Raw abundance")
#test order of the columns
if ( raw < experiment$StartColIntensities[fileNumber])
{
cat("\n", "Unexspected order of the columns Normalized abundance and Raw abundance!", "\n")
cat("Normalized abundance should be directly before Raw abundance!", "\n")
result <- FALSE
return(list(result,data,experiment))
}
else if (raw >= until )
{
cat("\n", "Unexspected order of the columns Raw abundance Spectral counts!", "\n")
cat("Raw abundance should be directly before Spectral counts!", "\n")
result <- FALSE
return(list(result,data,experiment))
}
else
{
#delete Raw Abundance from data
headers[ ,(raw:until)] <- list(NULL)
}
}
ncolHeaders <- ncol(headers)
experiment$StartColCounts[fileNumber] <- which(headers[1,]=="Spectral counts")
distCntInt <- experiment$StartColCounts[fileNumber] - experiment$StartColIntensities[fileNumber]
sequenceCol <- which(headers[3,]=="Sequence")
accessionCol <- which(headers[3,]=="Accession")
numExpGroups <- length(which(headers[2,]!=""))
numExpGroups <- numExpGroups / 2
expGroups <- headers[2,][headers[2,]!=""][1:numExpGroups]
expNamesTmp <- headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)]
experiment$ColumnNamesInt[fileNumber] <- list(paste0("Intensity_",
headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)]))
experiment$ColumnNamesCnt[fileNumber] <- list(paste0("Counts_",
headers[3,experiment$StartColCounts[fileNumber]:ncolHeaders]))
headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)] <-
unlist(experiment$ColumnNamesInt[fileNumber])
headers[3,experiment$StartColCounts[fileNumber]:ncolHeaders] <-
unlist(experiment$ColumnNamesCnt[fileNumber])
headers[3,] <- gsub(" ", "_", headers[3,])
# Dimensions of data.frame
headersRows <- nrow(headers)
headersCols <- ncol(headers)
experiment$ExpNames[fileNumber] <- list(expNamesTmp)
data <- read.table(file=fileName, check.names=TRUE, sep=',',
header=FALSE, stringsAsFactors=FALSE, skip=3)
#if there are raw abundances in the data
if (raw > 0)
{
#delete Raw Abundance from data
data[ ,(raw:until)] <- list(NULL)
}
#print(bla)
colnames(data) <- headers[3,]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
intensities <- data[,(experiment$StartColIntensities[fileNumber]):
(experiment$StartColCounts[fileNumber]-1)]
counts <- data[,experiment$StartColCounts[fileNumber]:dataCols-1]
#hier wird auch wieder etwas abgeschnitten
#data <- data[,1:dataCols-1]
data <- data[,1:dataCols]
data <- data.table(data)
experiment$NumExps[fileNumber] <- distCntInt
setnames(data, "Use_in_quantitation", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
#data[, Unique:=as.logical(Unique)]
#try to avoid no visible binding
#print(bla)
#setattr(data, "Unique", logical)
#data[, "Unique":=as.logical("Unique")]
data$Unique <- as.logical(data$Unique)
setkey(data, "Accession", "Sequence")
## Get Counts and Intensities
DTintensities <- data[, lapply(.SD, sum), by=c("Accession", "Sequence"),
.SDcols=(experiment$StartColIntensities[fileNumber]):
(experiment$NumExps[fileNumber]+
experiment$StartColIntensities[fileNumber]-1)]
#ACHTUNG der Bereich von .SDcols ist groesser als die data.table data
# Browse[1]> (experiment$StartColCounts[fileNumber]):
# + (experiment$NumExps[fileNumber]+
# + experiment$StartColCounts[fileNumber]-1)
# [1] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
# Browse[1]> length(colnames(data))
# [1] 41
DTcounts <- data[, lapply(.SD, sum), by=c("Accession", "Sequence"),
.SDcols=(experiment$StartColCounts[fileNumber]):
(experiment$NumExps[fileNumber]+
experiment$StartColCounts[fileNumber]-1)]
## Get column IDs from columns with annotations
annotationCols <- 1:(experiment$StartColIntensities[fileNumber]-1)
## Exclude Unique column
annotationCols <- annotationCols[-(which(colnames(data)=="Unique"))]
DTannotations <- data[, lapply(.SD, list),
by=c("Accession", "Sequence"), .SDcols=annotationCols]
DTannotations$Accession <-
#substring(data[, list(Accession=paste(Accession, collapse="")), by=c("Accession", "Sequence")]$Accession, 1,6)
#try to avoid binding variable warning
substring(data[, list("Accession"=paste("Accession", collapse="")),
by=c("Accession", "Sequence")]$Accession, 1,6)
DTannotations <- DTannotations[,lapply(.SD, as.character), by=c("Accession", "Sequence")]
#print(bla)
DTunique <- data[, lapply(.SD, all),
by=c("Accession", "Sequence"), .SDcols="Unique"]
data <- cbind(DTannotations, DTunique, DTintensities, DTcounts)
## Remove additional Sequence columns
removeCols <- which(colnames(data)=="Sequence")
data <- data[,!removeCols[-1] , with=FALSE]
## Remove additional Accession columns
removeCols <- which(colnames(data)=="Accession")
data <- data[,!removeCols[-1] , with=FALSE]
#data[,dataIndex:=(1:nrow(data))]
#try to avoid warning
data[,"dataIndex":=(1:nrow(data))]
setnames(data, paste0(colnames(data), "_File", fileNumber))
experiment$ColumnNamesInt[fileNumber] <-
list(paste0(experiment$ColumnNamesInt[[fileNumber]], "_File", fileNumber))
experiment$ColumnNamesCnt[fileNumber] <-
list(paste0(experiment$ColumnNamesCnt[[fileNumber]], "_File", fileNumber))
result <- TRUE
return(list(result, data, experiment))
}
# open csv file of an experiment for the secscoreWrapper
# Input: character vector with the path of the csv-file (Progenesis)
# Output: data.table data
ss_openCSV <- function (path)
{
data <- {}
result <- FALSE
txt <- "- Starting CSV-read..."
print(txt)
headers <- read.table(file = path, check.names = FALSE, sep = ',', header = FALSE, stringsAsFactors = FALSE, nrows = 3)
# warum wird hier die letzte Spalte abgeschnitten? da stehen Daten drin!
# fuehrt weiter unten auch zu Folgefehlern
# headers <- headers[-ncolHeaders]
# ncolHeaders <- ncol(headers)
#remove columns taht contain NA
naheader <- grep("NA",headers[1,])
headers[ , naheader] <- NULL
StartColIntensities <- which(headers[1,] == "Normalized abundance")
#test if there are Raw abundance values
until <- which(headers[1,]=="Spectral counts")-1
raw <- 0
if(sum(headers[1,]=="Raw abundance",na.rm = TRUE) >= 1)
{
raw <- which(headers[1,]=="Raw abundance")
#test order of the columns
if ( raw < StartColIntensities)
{
cat("\n", "Unexspected order of the columns Normalized abundance and Raw abundance!", "\n")
cat("Normalized abundance should be directly before Raw abundance!", "\n")
result <- FALSE
return(result)
}
else if (raw >= until )
{
cat("\n", "Unexspected order of the columns Raw abundance Spectral counts!", "\n")
cat("Raw abundance should be directly before Spectral counts!", "\n")
result <- FALSE
return(result)
}
else
{
#delete Raw Abundance from data
headers[ ,(raw:until)] <- list(NULL)
}
}
ncolHeaders <- ncol(headers)
StartColCounts <- which(headers[1,]=="Spectral counts")
distCntInt <- StartColCounts - StartColIntensities
sequenceCol <- which(headers[3,]=="Sequence")
accessionCol <- which(headers[3,]=="Accession")
numExpGroups <- length(which(headers[2,]!=""))
numExpGroups <- numExpGroups / 2
expGroups <- headers[2,][headers[2,]!=""][1:numExpGroups]
expNamesTmp <- headers[3,StartColIntensities:
(StartColCounts-1)]
ColumnNamesInt <- list(paste0("Intensity_", headers[3, StartColIntensities : (StartColCounts-1)]))
ColumnNamesCnt <- list(paste0("Counts_", headers[3, StartColCounts:ncolHeaders]))
headers[3, StartColIntensities : (StartColCounts-1)] <- unlist(ColumnNamesInt)
headers[3,StartColCounts : ncolHeaders] <- unlist(ColumnNamesCnt)
headers[3,] <- gsub(" ", "_", headers[3,])
# Dimensions of data.frame
headersRows <- nrow(headers)
headersCols <- ncol(headers)
ExpNames <- list(expNamesTmp)
data <- read.table(file=path, check.names=TRUE, sep=',',
header=FALSE, stringsAsFactors=FALSE, skip=3)
#print(bla)
#remove NA columns (see above)
data[ ,naheader] <- NULL
#if there are raw abundances in the data
if (raw > 0)
{
#delete Raw Abundance from data
data[ ,(raw:until)] <- list(NULL)
}
colnames(data) <- headers[3,]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
intensities <- data[ ,(StartColIntensities) : (StartColCounts-1)]
counts <- data[ ,StartColCounts:dataCols-1]
#hier wird auch wieder etwas abgeschnitten
#data <- data[,1:dataCols-1]
data <- data[,1:dataCols]
colnames(data)[colnames(data) == "Use_in_quantitation"] <- "Unique"
data$Unique <- as.logical(data$Unique)
return(data)
}
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# Secretome Lysat Peptide Descriptor
#
# sourcefile functions
#--------------------------------------------------------------------------------------
# open txt file of an experiment fitted to the newest MaxQuant output
# Input: list experiment, numeric vector fileNumber indicating the fileName stored in experiment$FileName
# Output: list of data.table data, list experiment
openTXT <- function (experiment, fileNumber)
{
txt <- "- Starting TXT-read..."
#printTcl(txt)
print(txt)
expNamesTmp <- ""
data <- {}
fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
"Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id",
"Carbamidomethyl (C) site IDs","NEM site IDs")
header <- readLines(fileName,n=1)
header2 <- unlist(strsplit(header,"\t"))
data <- suppressWarnings(data.table::fread(fileName,
colClasses=list(character=characterCols[characterCols %in% header2]),
integer64 = "double"))
# replace all spaces
setnames(data, gsub(" ", "_", colnames(data)))
setnames(data, "Leading_razor_protein", "Accession")
setnames(data, "Unique_(Groups)", "Unique")
setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Delete rows with Reverse or Contaminat = "+"
#data <- data[Reverse != "+"]
#data <- data[Potential_contaminant != "+"]
# try to avoid warning
data <- data["Reverse" != "+"]
data <- data["Potential_contaminant" != "+"]
# Include index in table
#data[,dataIndex:=(1:nrow(data))]
#try to avoid warning
data[,"dataIndex":=(1:nrow(data))]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
# Change column name for the Accession numbers
# setnames(data, "Leading razor protein", "Accession")
# setnames(data, "Unique (Groups)", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
# set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
# set(data,which(data[,Unique]=="no"),"Unique","FALSE")
#try to avoid warning
#print(bla)
Unique <- NULL
set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
set(data,which(data[,Unique]=="no"),"Unique","FALSE")
data$Unique <- as.logical(data$Unique)
# setnames(data, gsub(" ", "_", colnames(data)))
# setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Extracting the number of different experimental groups.
# The 1st data row it is checked for "Normalized abundance" and "Spectral counts".
# The 2nd row is checked for the number and the names of experimental groups.
expColsInt <- which(substring((colnames(data)),1, 10) == "Intensity_")
experiment$StartColIntensities[fileNumber] <- head(expColsInt, 1)
expColsCnt <- which(substring((colnames(data)),1, 7) == "Counts_")
experiment$StartColCounts[fileNumber] <- head(expColsCnt, 1)
experiment$NumExps[fileNumber] <- length(expColsInt)
if (length(length(expColsInt)) != length(length(expColsCnt)))
{
txt <- "- Warning: Experiment and Intensity numbers do not match ! ! !"
#tkinsert(txtWidget,"0.0",paste(txt, "\n"))
print(txt)
}
experiment$ExpNames[fileNumber] <- list(substring(colnames(data)[expColsInt], 11))
# suppressWarnings(
# for (j in names(data[,startColCounts[fileNumber]:(startColCounts[fileNumber]+(numExps[fileNumber]*2)-1), with=FALSE])){
# set(data,which(is.na(data[[j]])),j,0)
# }
# )
setnames(data, paste0(colnames(data), "_File", fileNumber))
experiment$ColumnNamesInt[fileNumber] <- list(colnames(data)[expColsInt])
experiment$ColumnNamesCnt[fileNumber] <- list(colnames(data)[expColsCnt])
return(list(data, experiment))
}
#--------------------------------------------
# open txt file of an experiment fittet to the newest MaxQuant output
# Input: path to peptide.txt
# Output: data.table data
ss_openTXT <- function (path)
{
txt <- "- Starting TXT-read..."
print(txt)
expNamesTmp <- ""
data <- {}
#fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
#characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
# "Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id")
characterCols <- c("Charges", "Oxidation (M) site IDs", "MS/MS IDs", "Evidence IDs", "Best MS/MS",
"Mod. peptide IDs", "Protein group IDs", "Reverse", "Potential contaminant", "id",
"Carbamidomethyl (C) site IDs","NEM site IDs")
header <- readLines(path,n=1)
header2 <- unlist(strsplit(header,"\t"))
data <- suppressWarnings(data.table::fread(path,
colClasses=list(character=characterCols[characterCols %in% header2]),
integer64 = "double"))
# data <- data.table::fread(path,
# colClasses=list(character=characterCols),
# integer64 = "double",verbose=TRUE)
# replace all spaces
setnames(data, gsub(" ", "_", colnames(data)))
#nnames <- gsub(" ","_", colnames(data))
# nnames(grep("Unique_\\(Groups\\)",nnames)) <- Unique
# nnames(grep("Leading_razor_protein",nnames)) <- Unique
setnames(data, "Leading_razor_protein", "Accession")
setnames(data, "Unique_(Groups)", "Unique")
setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Delete rows with Reverse or Contaminat = "+"
# data <- data[Reverse != "+"]
# data <- data[Potential_contaminant != "+"]
#try to avoid warning
data <- data["Reverse" != "+"]
data <- data["Potential_contaminant" != "+"]
# Include index in table
#data[,dataIndex:=(1:nrow(data))]
# try to avoid warnings
data[,"dataIndex":=(1:nrow(data))]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
# Change column name for the Accession numbers
# setnames(data, "Leading razor protein", "Accession")
# setnames(data, "Unique (Groups)", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
#set(data,which(data[,Unique]=="yes"),"Unique","TRUE")
#set(data,which(data[,Unique]=="no"),"Unique","FALSE")
#try to avoid warnings
#print(bla)
Unique <- NULL
set(data,which(data[, Unique]=="yes"),"Unique","TRUE")
set(data,which(data[, Unique]=="no"),"Unique","FALSE")
data$Unique <- as.logical(data$Unique)
#setattr(data, "Unique", logical)
#data[, "Unique":=as.logical("Unique")]
# setnames(data, gsub(" ", "_", colnames(data)))
# setnames(data, gsub("Experiment", "Counts", colnames(data)))
# Extracting the number of different experimental groups.
# The 1st data row it is checked for "Normalized abundance" and "Spectral counts".
# The 2nd row is checked for the number and the names of experimental groups.
expColsInt <- which(substring((colnames(data)),1, 10) == "Intensity_")
#experiment$StartColIntensities[fileNumber] <- head(expColsInt, 1)
expColsCnt <- which(substring((colnames(data)),1, 7) == "Counts_")
#experiment$StartColCounts[fileNumber] <- head(expColsCnt, 1)
#experiment$NumExps[fileNumber] <- length(expColsInt)
if (length(length(expColsInt)) != length(length(expColsCnt)))
{
txt <- "- Warning: Experiment and Intensity numbers do not match ! ! !"
print(txt)
}
return(data)
}
# open csv file of an experiment
# Input: list experiment, numeric vector fileNumber indicating the fileName stored in experiment$FileName
# Output: list of logical vector result, data.table data, list experiment
openCSV <- function (experiment, fileNumber)
{
data <- {}
result <- FALSE
txt <- "- Starting CSV-read..."
txt <- paste(txt, experiment$FileName[fileNumber])
print(txt)
fileName <- paste0(experiment$Path, experiment$FileName[fileNumber])
headers <- read.table(file = fileName, check.names = FALSE, sep = ',', header = FALSE, stringsAsFactors = FALSE, nrows = 3)
# warum wird hier die letzte Spalte abgeschnitten? da stehen Daten drin!
# fuehrt weiter unten auch zu Folgefehlern
# headers <- headers[-ncolHeaders]
# ncolHeaders <- ncol(headers)
experiment$StartColIntensities[fileNumber] <- which(headers[1,]=="Normalized abundance")
#test if there are Raw abundance values
until <- which(headers[1,]=="Spectral counts")-1
raw <- 0
if(sum(headers[1,]=="Raw abundance", na.rm = TRUE) >= 1)
{
raw <- which(headers[1,]=="Raw abundance")
#test order of the columns
if ( raw < experiment$StartColIntensities[fileNumber])
{
cat("\n", "Unexspected order of the columns Normalized abundance and Raw abundance!", "\n")
cat("Normalized abundance should be directly before Raw abundance!", "\n")
result <- FALSE
return(list(result,data,experiment))
}
else if (raw >= until )
{
cat("\n", "Unexspected order of the columns Raw abundance Spectral counts!", "\n")
cat("Raw abundance should be directly before Spectral counts!", "\n")
result <- FALSE
return(list(result,data,experiment))
}
else
{
#delete Raw Abundance from data
headers[ ,(raw:until)] <- list(NULL)
}
}
ncolHeaders <- ncol(headers)
experiment$StartColCounts[fileNumber] <- which(headers[1,]=="Spectral counts")
distCntInt <- experiment$StartColCounts[fileNumber] - experiment$StartColIntensities[fileNumber]
sequenceCol <- which(headers[3,]=="Sequence")
accessionCol <- which(headers[3,]=="Accession")
numExpGroups <- length(which(headers[2,]!=""))
numExpGroups <- numExpGroups / 2
expGroups <- headers[2,][headers[2,]!=""][1:numExpGroups]
expNamesTmp <- headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)]
experiment$ColumnNamesInt[fileNumber] <- list(paste0("Intensity_",
headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)]))
experiment$ColumnNamesCnt[fileNumber] <- list(paste0("Counts_",
headers[3,experiment$StartColCounts[fileNumber]:ncolHeaders]))
headers[3,experiment$StartColIntensities[fileNumber]:
(experiment$StartColCounts[fileNumber]-1)] <-
unlist(experiment$ColumnNamesInt[fileNumber])
headers[3,experiment$StartColCounts[fileNumber]:ncolHeaders] <-
unlist(experiment$ColumnNamesCnt[fileNumber])
headers[3,] <- gsub(" ", "_", headers[3,])
# Dimensions of data.frame
headersRows <- nrow(headers)
headersCols <- ncol(headers)
experiment$ExpNames[fileNumber] <- list(expNamesTmp)
data <- read.table(file=fileName, check.names=TRUE, sep=',',
header=FALSE, stringsAsFactors=FALSE, skip=3)
#if there are raw abundances in the data
if (raw > 0)
{
#delete Raw Abundance from data
data[ ,(raw:until)] <- list(NULL)
}
#print(bla)
colnames(data) <- headers[3,]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
intensities <- data[,(experiment$StartColIntensities[fileNumber]):
(experiment$StartColCounts[fileNumber]-1)]
counts <- data[,experiment$StartColCounts[fileNumber]:dataCols-1]
#hier wird auch wieder etwas abgeschnitten
#data <- data[,1:dataCols-1]
data <- data[,1:dataCols]
data <- data.table(data)
experiment$NumExps[fileNumber] <- distCntInt
setnames(data, "Use_in_quantitation", "Unique")
# Setting Unique as logical. FALSE + 0 = 0, TRUE + 0 = 1.
#data[, Unique:=as.logical(Unique)]
#try to avoid no visible binding
#print(bla)
#setattr(data, "Unique", logical)
#data[, "Unique":=as.logical("Unique")]
data$Unique <- as.logical(data$Unique)
setkey(data, "Accession", "Sequence")
## Get Counts and Intensities
DTintensities <- data[, lapply(.SD, sum), by=c("Accession", "Sequence"),
.SDcols=(experiment$StartColIntensities[fileNumber]):
(experiment$NumExps[fileNumber]+
experiment$StartColIntensities[fileNumber]-1)]
#ACHTUNG der Bereich von .SDcols ist groesser als die data.table data
# Browse[1]> (experiment$StartColCounts[fileNumber]):
# + (experiment$NumExps[fileNumber]+
# + experiment$StartColCounts[fileNumber]-1)
# [1] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
# Browse[1]> length(colnames(data))
# [1] 41
DTcounts <- data[, lapply(.SD, sum), by=c("Accession", "Sequence"),
.SDcols=(experiment$StartColCounts[fileNumber]):
(experiment$NumExps[fileNumber]+
experiment$StartColCounts[fileNumber]-1)]
## Get column IDs from columns with annotations
annotationCols <- 1:(experiment$StartColIntensities[fileNumber]-1)
## Exclude Unique column
annotationCols <- annotationCols[-(which(colnames(data)=="Unique"))]
DTannotations <- data[, lapply(.SD, list),
by=c("Accession", "Sequence"), .SDcols=annotationCols]
DTannotations$Accession <-
#substring(data[, list(Accession=paste(Accession, collapse="")), by=c("Accession", "Sequence")]$Accession, 1,6)
#try to avoid binding variable warning
substring(data[, list("Accession"=paste("Accession", collapse="")),
by=c("Accession", "Sequence")]$Accession, 1,6)
DTannotations <- DTannotations[,lapply(.SD, as.character), by=c("Accession", "Sequence")]
#print(bla)
DTunique <- data[, lapply(.SD, all),
by=c("Accession", "Sequence"), .SDcols="Unique"]
data <- cbind(DTannotations, DTunique, DTintensities, DTcounts)
## Remove additional Sequence columns
removeCols <- which(colnames(data)=="Sequence")
data <- data[,!removeCols[-1] , with=FALSE]
## Remove additional Accession columns
removeCols <- which(colnames(data)=="Accession")
data <- data[,!removeCols[-1] , with=FALSE]
#data[,dataIndex:=(1:nrow(data))]
#try to avoid warning
data[,"dataIndex":=(1:nrow(data))]
setnames(data, paste0(colnames(data), "_File", fileNumber))
experiment$ColumnNamesInt[fileNumber] <-
list(paste0(experiment$ColumnNamesInt[[fileNumber]], "_File", fileNumber))
experiment$ColumnNamesCnt[fileNumber] <-
list(paste0(experiment$ColumnNamesCnt[[fileNumber]], "_File", fileNumber))
result <- TRUE
return(list(result, data, experiment))
}
# open csv file of an experiment for the secscoreWrapper
# Input: character vector with the path of the csv-file (Progenesis)
# Output: data.table data
ss_openCSV <- function (path)
{
data <- {}
result <- FALSE
txt <- "- Starting CSV-read..."
print(txt)
headers <- read.table(file = path, check.names = FALSE, sep = ',', header = FALSE, stringsAsFactors = FALSE, nrows = 3)
# warum wird hier die letzte Spalte abgeschnitten? da stehen Daten drin!
# fuehrt weiter unten auch zu Folgefehlern
# headers <- headers[-ncolHeaders]
# ncolHeaders <- ncol(headers)
#remove columns taht contain NA
naheader <- grep("NA",headers[1,])
headers[ , naheader] <- NULL
StartColIntensities <- which(headers[1,] == "Normalized abundance")
#test if there are Raw abundance values
until <- which(headers[1,]=="Spectral counts")-1
raw <- 0
if(sum(headers[1,]=="Raw abundance",na.rm = TRUE) >= 1)
{
raw <- which(headers[1,]=="Raw abundance")
#test order of the columns
if ( raw < StartColIntensities)
{
cat("\n", "Unexspected order of the columns Normalized abundance and Raw abundance!", "\n")
cat("Normalized abundance should be directly before Raw abundance!", "\n")
result <- FALSE
return(result)
}
else if (raw >= until )
{
cat("\n", "Unexspected order of the columns Raw abundance Spectral counts!", "\n")
cat("Raw abundance should be directly before Spectral counts!", "\n")
result <- FALSE
return(result)
}
else
{
#delete Raw Abundance from data
headers[ ,(raw:until)] <- list(NULL)
}
}
ncolHeaders <- ncol(headers)
StartColCounts <- which(headers[1,]=="Spectral counts")
distCntInt <- StartColCounts - StartColIntensities
sequenceCol <- which(headers[3,]=="Sequence")
accessionCol <- which(headers[3,]=="Accession")
numExpGroups <- length(which(headers[2,]!=""))
numExpGroups <- numExpGroups / 2
expGroups <- headers[2,][headers[2,]!=""][1:numExpGroups]
expNamesTmp <- headers[3,StartColIntensities:
(StartColCounts-1)]
ColumnNamesInt <- list(paste0("Intensity_", headers[3, StartColIntensities : (StartColCounts-1)]))
ColumnNamesCnt <- list(paste0("Counts_", headers[3, StartColCounts:ncolHeaders]))
headers[3, StartColIntensities : (StartColCounts-1)] <- unlist(ColumnNamesInt)
headers[3,StartColCounts : ncolHeaders] <- unlist(ColumnNamesCnt)
headers[3,] <- gsub(" ", "_", headers[3,])
# Dimensions of data.frame
headersRows <- nrow(headers)
headersCols <- ncol(headers)
ExpNames <- list(expNamesTmp)
data <- read.table(file=path, check.names=TRUE, sep=',',
header=FALSE, stringsAsFactors=FALSE, skip=3)
#print(bla)
#remove NA columns (see above)
data[ ,naheader] <- NULL
#if there are raw abundances in the data
if (raw > 0)
{
#delete Raw Abundance from data
data[ ,(raw:until)] <- list(NULL)
}
colnames(data) <- headers[3,]
# Dimensions of data.frame
dataRows <- nrow(data)
dataCols <- ncol(data)
intensities <- data[ ,(StartColIntensities) : (StartColCounts-1)]
counts <- data[ ,StartColCounts:dataCols-1]
#hier wird auch wieder etwas abgeschnitten
#data <- data[,1:dataCols-1]
data <- data[,1:dataCols]
colnames(data)[colnames(data) == "Use_in_quantitation"] <- "Unique"
data$Unique <- as.logical(data$Unique)
return(data)
}
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