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RepAnFns.R
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RepAnFns.R
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### ***************************************************************
### ******** Basic analysis for TCR repertoire data **************
### ***************************************************************
########################### External R packages ###########################
#' Attaches packages from CRAN after installing them if they don't exist already
#'
#' @param package.list vector of packages
#'
loadPacks <- function(package.list = c("ggplot2","seqRFLP","stringr","permute","cluster","data.table","NbClust","doParallel","dendextend","kmer","ape","Matrix","wordspace","dynamicTreeCut","e1071","fclust","randomForest","preprocessCore","gplots")){
# other probably needed packages that have been removed for now: "Rclusterpp"
new.packages <-package.list[!(package.list %in% installed.packages()[,"Package"])]
if(length(new.packages) > 0) try(install.packages(new.packages))
loadSuccess <- lapply(eval(package.list), require, character.only=TRUE,quietly=TRUE)
}
#' Attaches packages from Bioconductor after installing them if they don't exist already
#'
#' @param package.list vector of Bioconductor packages
#'
loadBioconductorPacks <- function(package.list = c("Biostrings","RankProd","preprocessCore","msa","ggseqlogo")){
new.packages <-package.list[!(package.list %in% installed.packages()[,"Package"])]
if(length(new.packages) > 0){
if((R.Version()[["major"]] >= 4) | (R.Version()[["major"]] >= 3 & unlist(strsplit(R.Version()[["minor"]],"\\."))[1] >= 5)){
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(new.packages)
}else{
source("https://bioconductor.org/biocLite.R")
biocLite(new.packages)
}
}
loadSuccess <- lapply(eval(package.list), require, character.only=TRUE,quietly=TRUE)
}
#loadPacks()
#loadBioconductorPacks()
########################### FUNCTIONS #########################
## read data files ####
#' Read in Repertoire data (Repseq sample) in older Immunoseq format
#'
#' @param samplename name of the immunoseq formatted repertoire sample (full address or in the working directory)
#' @param cloneStatus logical; reading only productive or only unproductive CDR3s from the sample; default is productive only
#' @param filterOutBelowSize1 logical; drop clonotypes with a size of 1, default is true
#' @return a repseq sample is returned
#'
readSample <- function(samplename,cloneStatus="p",filterOutBelowSize1=T){
# read sample name and change colnames to uppercase
sam <- read.table(samplename, header=T, sep="\t",dec = ".",stringsAsFactors=F)
#sam <- as.data.frame(fread(samplename, header=T, sep="\t",stringsAsFactors=F))
colnames(sam) <- toupper(colnames(sam))
sam <- sam[,which(colnames(sam)=="NUCLEOTIDE"):length(colnames(sam))] # remove unwanted columns that come with some data e.g container etc
# Extract cdr3 sequences and put on one column
CDR3NT = substring(sam$NUCLEOTIDE,sam$VINDEX+1,sam$VINDEX + sam$CDR3LENGTH)
sam <- data.frame(sam,CDR3NT,stringsAsFactors=F)
# if there is a column named copy, change it to COUNT
if(sum(colnames(sam) == "COPY") == 1){
colnames(sam)[which(colnames(sam) == "COPY")] <- "UNNORMALIZEDCOUNT"
colnames(sam)[which(colnames(sam) == "NORMALIZEDCOPY")] <- "COUNT"
colnames(sam)[which(colnames(sam) == "NORMALIZEDFREQUENCY")] <- "FREQUENCYCOUNT"
}
if(filterOutBelowSize1==T)
sam <- sam[sam[,"COUNT"] > 1,]
# filter sample for only productive or unproductive clonotypes
if(cloneStatus == "p"){
sam = getProductive(sam)
}else if(cloneStatus == "unp"){
sam = getUnproductive(sam)
}
return(sam)
}
#' Read in Repertoire data (Repseq sample) in the recent (version 3) Immunoseq format
#'
#' @param samplename name of the immunoseq formatted repertoire sample (full address or in the working directory)
#' @param cloneStatus logical; reading only productive or only unproductive CDR3s from the sample; default is productive only
#' @param filterOutBelowSize1 logical; drop clonotypes with a size of 1, default is true
#' @return a repseq sample is returned
#'
readSampleV2 <- function(samplename,cloneStatus="p",filterOutBelowSize1=T){
# read sample name and change colnames to uppercase
#sam <- read.table(samplename, header=T, sep="\t",dec = ".",stringsAsFactors=F)
#sam <- as.data.frame(fread(samplename, header=T, sep="\t",stringsAsFactors=F,verbose=F,showProgress=F))
sam <- as.data.frame(fread(samplename, header=T, sep="\t",dec = ".",stringsAsFactors=F))
#sam <- read.table(samplename, header=T, sep="\t",dec = ".",stringsAsFactors=F)
colnames(sam) <- toupper(colnames(sam))
sam <- sam[,which(colnames(sam)=="NUCLEOTIDE"):length(colnames(sam))] # remove unwanted columns that come with some data e.g container etc
# Extract cdr3 sequences and put on one column
CDR3NT = substring(sam$NUCLEOTIDE,sam$VINDEX+1,sam$VINDEX + sam$CDR3LENGTH)
sam <- data.frame(sam,CDR3NT,stringsAsFactors=F)
# if there is a column named copy, change it to COUNT
colnames(sam)[grep("TEMPLATES",colnames(sam))] <- "COUNT"
colnames(sam)[grep("FREQUENCYCOUNT",colnames(sam))] <- "FREQUENCYCOUNT"
if(filterOutBelowSize1==T)
sam <- sam[sam[,"COUNT"] > 1,]
#colnames(sam)[grep("AMINOACID",colnames(sam))] <- "AMINOACID"
#colnames(sam)[grep("VGENENAME",colnames(sam))] <- "VGENENAME"
#colnames(sam)[grep("SEQUENCESTATUS",colnames(sam))] <- "SEQUENCESTATUS"
# filter sample for only productive or unproductive clonotypes
if(cloneStatus == "p"){
sam = getProductive(sam)
}else if(cloneStatus == "unp"){
sam = getUnproductive(sam)
}
return(sam)
}
#' Read in MiXCR format data and change to Immunoseq format
#'
#' @param samplename name of the MiXCR formatted repertoire sample (full address or in the working directory)
#' @param cloneStatus logical; reading only productive or only unproductive CDR3s from the sample; default is productive only
#' @param filterOutBelowSize1 logical; drop clonotypes with a size of 1, default is true
#' @return a repseq sample is returned
#'
readMiXCR <- function(samplename,cloneStatus="p",filterOutBelowSize1=T){
# read the file fast, these are big files usually
sam <- as.data.frame(data.table::fread(samplename, header=T, dec = ".",stringsAsFactors=F))
# we drop clones with size 1 at this point since these data are huge, in addition Immunoseq min clone size is 2.
if(filterOutBelowSize1==T)
sam <- sam[sam[,"Clone count"] > 1,]
sam$CDR3length <- sapply(sam[,"N. Seq. CDR3"],nchar)
sam$vgenename <- sapply(sam[,"All V hits"],function(x) unlist(strsplit(unlist(strsplit(x,","))[1],"\\*"))[1])
sam$vgenename[is.na(sam$vgenename)] <- "unresolved"
sam$dgenename <- sapply(sam[,"All D hits"],function(x) unlist(strsplit(unlist(strsplit(x,","))[1],"\\*"))[1])
sam$dgenename[is.na(sam$dgenename)] <- "unresolved"
sam$jgenename <- sapply(sam[,"All J hits"],function(x) unlist(strsplit(unlist(strsplit(x,","))[1],"\\*"))[1])
sam$jgenename[is.na(sam$jgenename)] <- "unresolved"
# continue by figuring out the stop and out of frame clones, then reorder the columns, then name them using immunoseq names.Then return sample
#sam[,"AA. Seq. CDR3"]
sam$SEQUENCESTATUS <- "In"
sam$SEQUENCESTATUS[grep("\\*",sam[,"AA. Seq. CDR3"])] <- "Stop"
reOrderColumnsToImmunoseqFormat <- c("Clonal sequence(s)","AA. Seq. CDR3","Clone count",
"Clone fraction","CDR3length","vgenename","dgenename","jgenename","SEQUENCESTATUS","N. Seq. CDR3")
sam <- sam[,reOrderColumnsToImmunoseqFormat]
colnames(sam) <- c("NUCLEOTIDE","AMINOACID","COUNT","FREQUENCYCOUNT","CDR3LENGTH","VGENENAME","DGENENAME","JGENENAME","SEQUENCESTATUS","CDR3NT")
colnames(sam) <- toupper(colnames(sam))
# filter sample for only productive or unproductive clonotypes
if(cloneStatus == "p"){
sam = getProductive(sam)
}else if(cloneStatus == "unp"){
sam = getUnproductive(sam)
}
return(sam)
}
renameCopyTOCount <- function(samplename){
# change some variable names
for(rep in reps){
sam <- get(rep)
if(sum(colnames(sam) == "COPY") == 1){
colnames(sam)[which(colnames(sam) == "COPY")] <- "UNNORMALIZEDCOUNT"
colnames(sam)[which(colnames(sam) == "NORMALIZEDCOPY")] <- "COUNT"
colnames(sam)[which(colnames(sam) == "NORMALIZEDFREQUENCY")] <- "FREQUENCYCOUNT"
}
}
}
extractCDR3 <- function(samplename){
for(rep in reps){
p <- get(rep)
CDR3NT = substring(p$NUCLEOTIDE,p$VINDEX,p$VINDEX+p$CDR3LENGTH)
p <- data.frame(p,CDR3NT)
}
}
#### Important Data related functions : eg. extract productive clones only etc. ####
#' Get productive only CDR3s from a repertoire data (Repseq sample) in immunoseq format
#'
#' @param rep name of the repertoire sample (which has already been read in with for example with read.table)
#' @return a subset of the repertoire (rep) containing only productive CDR3s is returned
#'
getProductive <- function(rep) {
p <- rep[rep$SEQUENCESTATUS=="Productive" | rep$SEQUENCESTATUS=="In",]
return(p)
}
#' Get unproductive CDR3s from a repertoire data (Repseq sample) in immunoseq format
#'
#' @param rep name of the repertoire sample (which has already been read in with for example with read.table)
#' @return a subset of the repertoire (rep) containing only unproductive CDR3s is returned
#'
getUnproductive <- function(rep) {
p <- rep[!(rep$SEQUENCESTATUS=="Productive" | rep$SEQUENCESTATUS=="In"),]
return(p)
}
#' Get out of frame CDR3s from a repertoire data (Repseq sample) in immunoseq format
#'
#' @param rep name of the repertoire sample (which has already been read in with for example with read.table)
#' @return a subset of the repertoire (rep) containing only out of frame CDR3s is returned
#'
getOutOfFrame <- function(rep) {
p <- rep[rep$SEQUENCESTATUS=="Out of frame" | rep$SEQUENCESTATUS=="Out",]
return(p)
}
#' Get stop codon containing CDR3s from a repertoire data (Repseq sample) in immunoseq format
#'
#' @param rep name of the repertoire sample (which has already been read in with for example with read.table)
#' @return a subset of the repertoire (rep) containing CDR3s with stop codon is returned
#'
getHasStop <- function(rep) {
p <- rep[rep$SEQUENCESTATUS=="Has stop" | rep$SEQUENCESTATUS=="Stop",]
return(p)
}
#' Get CDR3s that have well defined V gene name from a repertoire data (Repseq sample) in immunoseq format
#'
#' @param rep name of the repertoire sample (which has already been read in with for example with read.table)
#' @return a subset of the repertoire (rep) containing CDR3s with well defined v gene name is returned
#'
getDefined <- function(rep) {
p <- rep[rep$VGENENAME != "(undefined)" | rep$VGENENAME!= "unresolved",]
return(p)
}
#### Getting all V and J genes in our data: ####
getAllVgenes <- function(reps)
{
vgenes_GB=c()
for(rep in reps){
p <- get(rep)
vgenes_GB=union(vgenes_GB,getUniqueVgenes(p))
}
return(sort(vgenes_GB))
}
getAllVfamilies <- function(reps)
{
vfam=c()
for(rep in reps){
p <- get(rep)
vfam=union(vfam,getUniqueVfamilies(p))
}
return(sort(vfam))
}
getAllJgenes <- function(reps)
{
jgenes_GB=c()
for(rep in reps){
p <- get(rep)
jgenes_GB=union(jgenes_GB,getUniqueJgenes(p))
}
return(sort(jgenes_GB))
}
getAllVJgenes <- function(reps)
{
jgenes=getAllJgenes(reps)
vgenes=getAllVgenes(reps)
## All possible VJ combinations
vj <- expand.grid(vgenes,jgenes)
colnames(vj) <- c("VGENENAME","JGENENAME")
return(vj)
}
getUniqueVgenes <- function(rep) {
p <- rep[rep$VGENENAME != "(undefined)" | rep$VGENENAME != "unresolved",]
return(unique(p$VGENENAME)) # [-1] -1 inorder to drop the undefined Vgene type
}
getUniqueVfamilies <- function(rep) {
p <- rep[rep$VFAMILYNAME != "(undefined)" | rep$VFAMILYNAME != "unresolved",]
return(unique(p$VFAMILYNAME)) # [-1] -1 inorder to drop the undefined Vgene type
}
getUniqueVfamilies_PBMC <- function(rep) {
p <- rep[rep$VFAMILYNAME != "(undefined)" | rep$VFAMILYNAME != "unresolved",]
return(unique(p$VFAMILYNAME)) # [-1] -1 inorder to drop the undefined Vgene type
}
getUniqueJgenes <- function(rep) {
p <- rep[rep$JGENENAME != "(undefined)" | rep$JGENENAME != "unresolved",]
return(unique(p$JGENENAME))
}
#### V, J or VJ usage counts and proportions :
# clone level
getVgeneCloneCountPMatrix<- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The proportions of available clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data.
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
print (nrow(p))
#p <- getDefined(p)
vfamAgg <- aggregate(AMINOACID~ VGENENAME ,data = p, FUN = length)
vfamAgg[,2] <- vfamAgg[,2]/sum(vfamAgg[,2]) # get the proportion
colnames(vfamAgg) <- c("VGENENAME",rep)
m <- merge(m,vfamAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneCloneCountMatrix <- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The number of available clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
print (nrow(p))
#p <- getDefined(p)
vgeneAgg <- aggregate(AMINOACID~ VGENENAME ,data = p, FUN = length)
colnames(vgeneAgg) <- c("VGENENAME",rep)
m <- merge(m,vgeneAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneCloneCountPMatrixUnprod<- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The proportions of available unproductive clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
print (nrow(p))
#p <- getDefined(p)
vfamAgg <- aggregate(AMINOACID~ VGENENAME ,data = p, FUN = length)
vfamAgg[,2] <- vfamAgg[,2]/sum(vfamAgg[,2]) # get the proportion
colnames(vfamAgg) <- c("VGENENAME",paste(rep))
m <- merge(m,vfamAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneCloneCountMatrixUnprod <- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The number of available unproductive clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
print (nrow(p))
#p <- getDefined(p)
vgeneAgg <- aggregate(AMINOACID~ VGENENAME ,data = p, FUN = length)
colnames(vgeneAgg) <- c("VGENENAME",rep)
m <- merge(m,vgeneAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjCloneCountMatrix<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene combinations. The remaining columns show
### The number of available clones using each vj gene combination for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(AMINOACID~ VGENENAME + JGENENAME ,data = p, FUN = length)
print(head(vjPairAgg))
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjCloneCountMatrixUnprod<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The number of available unproductive clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(AMINOACID~ VGENENAME + JGENENAME ,data = p, FUN = length)
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjCloneCountPMatrix<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The proportions of available clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(AMINOACID~ VGENENAME + JGENENAME ,data = p, FUN = length)
vjPairAgg[,3] <- vjPairAgg[,3]/sum(vjPairAgg[,3]) # get the proportion
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjCloneCountPMatrixUnprod<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The proportions of available unproductive clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(AMINOACID~ VGENENAME + JGENENAME ,data = p, FUN = length)
vjPairAgg[,3] <- vjPairAgg[,3]/sum(vjPairAgg[,3]) # get the proportion
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
# read level
getVgeneReadCountPMatrix<- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The proportions of available clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data.
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
print (nrow(p))
#p <- getDefined(p)
vfamAgg <- aggregate(COUNT~ VGENENAME ,data = p, FUN = sum)
vfamAgg[,2] <- vfamAgg[,2]/sum(vfamAgg[,2]) # get the proportion
colnames(vfamAgg) <- c("VGENENAME",rep)
m <- merge(m,vfamAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneReadCountMatrix <- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The number of available clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
print (nrow(p))
#p <- getDefined(p)
vgeneAgg <- aggregate(COUNT~ VGENENAME ,data = p, FUN = sum)
colnames(vgeneAgg) <- c("VGENENAME",rep)
m <- merge(m,vgeneAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneReadCountPMatrixUnprod<- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The proportions of available unproductive clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
print (nrow(p))
#p <- getDefined(p)
vfamAgg <- aggregate(COUNT~ VGENENAME ,data = p, FUN = sum)
vfamAgg[,2] <- vfamAgg[,2]/sum(vfamAgg[,2]) # get the proportion
colnames(vfamAgg) <- c("VGENENAME",paste(rep))
m <- merge(m,vfamAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVgeneReadCountMatrixUnprod <- function(reps,g){
### Returns a datamatrix whose first two colums show the V gene. The remaining columns show
### The number of available unproductive clones using each v gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- as.data.frame(g)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
print (nrow(p))
#p <- getDefined(p)
vgeneAgg <- aggregate(COUNT~ VGENENAME ,data = p, FUN = sum)
colnames(vgeneAgg) <- c("VGENENAME",rep)
m <- merge(m,vgeneAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjReadCountMatrix<- function(reps,vj){
### Returns a datamatrix whose first two colums show the vj gene. The remaining columns show
### The number of available clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(COUNT~ VGENENAME + JGENENAME ,data = p, FUN = sum)
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjReadCountMatrixUnprod<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The number of available unproductive clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(COUNT~ VGENENAME + JGENENAME ,data = p, FUN = sum)
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjReadCountPMatrix<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The proportions of available clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getProductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(COUNT~ VGENENAME + JGENENAME ,data = p, FUN = sum)
vjPairAgg[,3] <- vjPairAgg[,3]/sum(vjPairAgg[,3]) # get the proportion
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
getVjReadCountPMatrixUnprod<- function(reps,vj){
### Returns a datamatrix whose first two colums show the Vj gene. The remaining columns show
### The proportions of available unprod. clones using each vj gene for each sample
#reps = a vector of dataframe names. The data frames are TCR repertoire data
m <- vj
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
p <- getUnproductive(p)
#p <- getDefined(p)
vjPairAgg <- aggregate(COUNT~ VGENENAME + JGENENAME ,data = p, FUN = sum)
vjPairAgg[,3] <- vjPairAgg[,3]/sum(vjPairAgg[,3]) # get the proportion
colnames(vjPairAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,vjPairAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]
rownames(m) <- m[,1]
m <- m[,-1]
return(m)
}
# General Counting function for V, J, VJ genes for each sequence status ####
getAggregateCount <- function(reps,countfor="V",seqStatus="IN",lev="A",countsorpro="prop"){
### Returns a datamatrix whose first two colums show the features The remaining columns show
### The number of available clones for each feature :
#reps - a vector of dataframe names. The data frames are TCR repertoire data.
# countfor - the features to be counted, default V for VgeneNames, vf for V family, J for J gene, VJ for vj genes
# seqStatus - for which sequences is the count to be done. default In for productive, Out for out of frame, Stop for stop codon containing sequences
# lev - at which level is the counting to be done . default A for Amino acid, N for nucleotide level
# countsorpro - return counts raw counts or proportions
# setting work parameters
countfor = toupper(countfor)
seqStatus= toupper(seqStatus)
lev = toupper(lev)
countsorpro= toupper(countsorpro)
print(countfor)
if(countfor=="V"){
features= getAllVgenes(reps)
m <- as.data.frame(features)
colnames(m) <- c("VGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
if(seqStatus=="IN"){
p <- getProductive(p)
}else if(seqStatus=="OUT"){
p <- getOutOfFrame(p)
}else if(seqStatus=="STOP"){
p <- getHasStop(p)
}
print (nrow(p))
if(lev=="N"){
dAgg <- aggregate(NUCLEOTIDE~ VGENENAME ,data = p, FUN = length)
}else {
dAgg <- aggregate(AMINOACID~ VGENENAME ,data = p, FUN = length)
}
if(countsorpro=="PROP"){
dAgg[,2] <- dAgg[,2]/sum(dAgg[,2]) # get the proportion
}
colnames(dAgg) <- c("VGENENAME",rep)
m <- merge(m,dAgg, by="VGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
}
else if(countfor=="J"){
features= getAllJgenes(reps)
m <- as.data.frame(features)
colnames(m) <- c("JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
if(seqStatus=="IN"){
p <- getProductive(p)
}else if(seqStatus=="OUT"){
p <- getOutOfFrame(p)
}else if(seqStatus=="STOP"){
p <- getHasStop(p)
}
print (nrow(p))
if(lev=="N"){
dAgg <- aggregate(NUCLEOTIDE~ JGENENAME ,data = p, FUN = length)
}else {
dAgg <- aggregate(AMINOACID~ JGENENAME ,data = p, FUN = length)
}
if(countsorpro=="PROP"){
dAgg[,2] <- dAgg[,2]/sum(dAgg[,2]) # get the proportion
}
colnames(dAgg) <- c("JGENENAME",rep)
m <- merge(m,dAgg, by="JGENENAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
}
else if(countfor=="VF"){
features= getAllVfamilies(reps)
m <- as.data.frame(features)
colnames(m) <- c("VFAMILYNAME")
for(rep in reps){
print(rep)
p <- get(rep)
if(seqStatus=="IN"){
p <- getProductive(p)
}else if(seqStatus=="OUT"){
p <- getOutOfFrame(p)
}else if(seqStatus=="STOP"){
p <- getHasStop(p)
}
print (nrow(p))
if(lev=="N"){
dAgg <- aggregate(NUCLEOTIDE~ VFAMILYNAME ,data = p, FUN = length)
}else {
dAgg <- aggregate(AMINOACID~ VFAMILYNAME ,data = p, FUN = length)
}
if(countsorpro=="PROP"){
dAgg[,2] <- dAgg[,2]/sum(dAgg[,2]) # get the proportion
}
colnames(dAgg) <- c("VFAMILYNAME",rep)
m <- merge(m,dAgg, by="VFAMILYNAME",all.x=T)
}
m[is.na(m)] <- 0
rownames(m) <- m[,1]
m <- m[,-1]
}
else if(countfor=="VJ"){
features= getAllVJgenes(reps)
m <- features
colnames(m) <- c("VGENENAME","JGENENAME")
for(rep in reps){
print(rep)
p <- get(rep)
if(seqStatus=="IN"){
p <- getProductive(p)
}else if(seqStatus=="OUT"){
p <- getOutOfFrame(p)
}else if(seqStatus=="STOP"){
p <- getHasStop(p)
}
print (nrow(p))
if(lev=="N"){
dAgg <- aggregate(NUCLEOTIDE~ VGENENAME + JGENENAME ,data = p, FUN = length)
}else {
dAgg <- aggregate(AMINOACID~ VGENENAME + JGENENAME ,data = p, FUN = length)
}
if(countsorpro=="PROP"){
dAgg[,3] <- dAgg[,3]/sum(dAgg[,3]) # get the proportion
}
colnames(dAgg) <- c("VGENENAME","JGENENAME",rep)
m <- merge(m,dAgg, by=c("VGENENAME","JGENENAME"),all.x=T)
}
m[is.na(m)] <- 0
m[,1]<-paste(m[,1],m[,2])
m<-m[,-2]