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K.024-04-29_TCGA.Rmd
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K.024-04-29_TCGA.Rmd
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---
title: "J1994. Compare TCRBV_CDR3 info with TCGA"
author: Ludmila Danilova
date: "`r Sys.Date()`"
output:
html_document:
toc: yes
toc_float: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE,
warning = FALSE, cache = TRUE)
rm(list = ls())
library(openxlsx)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(data.table)
library("DT")
library(kableExtra)
library(maftools)
library(MatrixGenerics)
library(stringdist)
geti = function(x,i){x[i]}
#===========================
# finds common samples using the first 15 characters from barcode
getCommonSamples = function(samples1, samples2, nameStart = 6, nameEnd = 15, uniq = T)
{
# finds common samples
comSamples = intersect(substr(samples1,nameStart,nameEnd),substr(samples2,nameStart,nameEnd))
# print(comSamples)
# finds ids of common samples for meth and expression datasets
methSamp = vector()
exprSamp = vector()
for (samp in comSamples)
{
ind1 = grep(samp, samples1)
ind2 = grep(samp, samples2)
if (length(ind1)>0 && length(ind2)>0)
{
if (uniq)
{
methSamp = c(methSamp, samples1[ind1[1]])
exprSamp = c(exprSamp, samples2[ind2[1]])
}else{
methSamp = c(methSamp, samples1[ind1])
exprSamp = c(exprSamp, samples2[ind2])
}
}
}
return (list("samp1" = methSamp,"samp2" = exprSamp))
}
# extract V gene and aaSeq and combine as TRBV_CDR3
# to be able to compare with J1994 data
getPairs = function(dat)
{
tcgaPairs = vector(length = nrow(dat))
for(i in 1:nrow(dat))
{
# split by comma
allV = unlist(strsplit(dat[i,"vAllele"], ", "))
# remove "TR" to be consistent with J1994 data
allV = gsub("TR", "", allV)
# combine with aaSeq
tcgaPairs[i] = paste(allV,dat[i,"aaSeq"], sep = "_")
}
return(unique(tcgaPairs))
}
```
# Intput data
Per patient clones are in 2024-04-18_all-filtered-diffex.tsv
TCGA TCR CDR3 data is in TCGA_mitcr_cdr3_result_161008.tsv from dbGaP (https://www.cell.com/immunity/fulltext/S1074-7613(18)30121-3).
Unfortunately this cannot be shared directly and must be downloaded from dbGaP.
Analysis plan:
1. Take only beta chain in TCGA and PAAD and compare pairs
2. Can check other cancer types.
Couldn't find anything in common for pairs.
Found one common CDR3 when compare with all TCGA samples
```{r}
# read in TCGA data
tcgaData = read.table("C:/Users/Luda/OneDrive - Johns Hopkins/JHU/TCGA/Immune_landscape/Optitype_neoantigens/TCGA_mitcr_cdr3_result_161008.tsv", header = T, sep = "\t")
# filter beta chain and PAAD samples only
#paadPairs = getPairs(tcgaData %>% filter(chain == "beta" & Study == "PAAD"))
#saveRDS(paadPairs, "paad_tcrv_cdr3_pairs.rds")
paadPairs = readRDS("paad_tcrv_cdr3_pairs.rds")
# all tumor types, beta only
#tcgaPairs = getPairs(tcgaData %>% filter(chain == "beta"))
#saveRDS(tcgaPairs, "tcga_tcrv_cdr3_pairs.rds")
tcgaPairs = readRDS("tcga_tcrv_cdr3_pairs.rds")
#====================
# read in J1994 data
pat_data = read.table("../current_tables/2024-04-18_all-filtered-diffex.tsv",sep='\t', header = T)
# create TCRgene_clone pairs to map to TCGA data
# remove "TCR" to be consistent with TCGA
id = substr(pat_data$TRBV, 4, 1000000L)
# remove 0 in the first part of the names before *
# split, remove, combine back
id1 = sapply(strsplit(id, "*", fixed = T), geti, 1)
# remove 0
id1 = gsub("0","", id1)
id2 = sapply(strsplit(id, "*", fixed = T), geti, 2)
# combine back
id = paste(id1,id2,sep = "*")
# replace artifact "*NA" with ""
id = gsub("*NA","", id, fixed = T)
# add pairs to the data
pat_data$fixed_pair = paste(id,pat_data$CDR3.beta.aa, sep= "_")
# take unique only
patPairs = lapply(pat_data$fixed_pair,unique)
# unique V genes
#sapply(pat_data, function(x)unique(sapply(strsplit(x,"_"),geti,1)))
#===============
# intersect TCRV_CDR3 pairs TCGA and J1994 data
length(intersect(patPairs,tcgaPairs)) # 27
#==========================
# compare AA sequences only
# unique AA seq in J1994
aaPat = unique(pat_data$CDR3.beta.aa)# 5437
length(aaPat)
# unique AA seq in TCGA
aaTcga = sapply(strsplit(tcgaPairs,"_"),geti,2) #
aaTcga = unique(aaTcga) # 186251
length(aaTcga)
# unique AA seq in PAAD
aaPAAD = sapply(strsplit(paadPairs,"_"),geti,2) #
aaPAAD = unique(aaPAAD) # 186251
length(aaPAAD)
# intersection
commonAA = intersect(aaPat,aaTcga)
length(commonAA) # 377
commonSamp = tcgaData[which(tcgaData$aaSeq %in% commonAA), ]
commonSamp_noDup = commonSamp %>% filter(!duplicated(SampleBarcode))
table(commonSamp_noDup$Study)
# check PAAD only
aaPAAD = sapply(strsplit(paadPairs,"_"),geti,2) #
aaPAAD = unique(aaPAAD)
commonAA_paad = intersect(aaPat,aaPAAD)
length(commonAA_paad) # 8
commonSamp_paad = tcgaData[which(tcgaData$aaSeq %in% commonAA_paad), ]
commonSamp_paad = commonSamp_paad %>% filter(!duplicated(SampleBarcode))
table(commonSamp_paad$Study)
```
```{r eval = F, echo=FALSE}
#============================
# What type of mutations corresponds to that common AA seq
# check mutations for those samples
# load MC3
# mc3 = readRDS("C:/Users/Luda/OneDrive - Johns Hopkins/JHU/Articles/TCGA/MC3-PublicMAF/public_mc3_maf.rds")
#
# # get common samples
# comm = getCommonSamples(mc3@clinical.data$Tumor_Sample_Barcode, commonSamp$SampleBarcode)
# # subset MAF to those samples
# maf_commonSamp = subsetMaf(mc3, tsb = comm[[1]])
# saveRDS(maf_commonSamp, "maf_commonSamp.rds")
maf_commonSamp = readRDS("maf_commonSamp.rds")
genes = getGeneSummary(maf_commonSamp)
dim(genes)
oncoplot(maf = maf_commonSamp, genes = c("KRAS","TP53"),showTumorSampleBarcodes = T)
# PAAD samples only
comm = getCommonSamples(mc3@clinical.data$Tumor_Sample_Barcode, commonSamp_paad %>% filter(Study == "PAAD") %>% select(SampleBarcode) %>% unlist)
maf_commonSamp_paad = subsetMaf(mc3, tsb = comm[[1]])
dim(getGeneSummary(maf_commonSamp_paad))
# extract KRAS mutations only
paadKRAS = maf_commonSamp_paad@data %>% filter(Hugo_Symbol == "KRAS") %>% select(Hugo_Symbol,Variant_Classification, Tumor_Sample_Barcode, HGVSc, HGVSp, HGVSp_Short ) #
paadKRAS$ParticipantBarcode = substr(paadKRAS$Tumor_Sample_Barcode,1,12)
paadKRAS$HGVSp_Short = gsub("p.","", paadKRAS$HGVSp_Short, fixed = T)
# 1: p.Gln61His Q61H TCGA-IB-7649
# 2: p.Gly12Asp G12D TCGA-IB-A5SS
# 3: p.Gly12Asp G12D TCGA-IB-A7LX
```
# Compare AA sequences with 2 mismatches
Compare CDR3 in J1994 and TCGA
From TCGA, take only pairs from PAAD samples
```{r}
aaTcga = unique(sapply(strsplit(paadPairs,"_"),geti,2)) #
length(aaTcga)
# find pairwise distances between AA sequences in patients and TCGA
set.seed(12345)
dists = stringdistmatrix(aaPat,aaTcga, "lv")
rownames(dists) = aaPat
colnames(dists) = aaTcga
# find seq with 1-2 mismatches in pat
# per row minimum in patients
m = rowMins(dists, na.rm = T, useNames = T)
summary(m)
minSeqPat = names(m)[which(m < 3)]
length(minSeqPat)
# find seq with 1-2 mismatches in TCGA
m = colMins(dists, na.rm = T, useNames = T)
names(m) = aaTcga
minSeqTcga = names(m)[which(m < 3)]
length(minSeqTcga)
# get info about samples with those AA seq
paadBeta = tcgaData %>% filter(chain == "beta" & Study == "PAAD")
commonSamp_2MM = paadBeta[which(paadBeta$aaSeq %in% minSeqTcga), ]
commonSamp_2MM_noDup = commonSamp_2MM %>% filter(!duplicated(SampleBarcode))
# distribution of samples across studies
table(commonSamp_2MM_noDup$Study)
# Take PAAD samples
paadSamp = commonSamp_2MM_noDup$SampleBarcode
length(paadSamp)
#
comm = getCommonSamples(mc3@clinical.data$Tumor_Sample_Barcode, paadSamp)
maf_paadSamp = subsetMaf(mc3, tsb = comm[[1]])
# saveRDS(maf_paadSamp, "maf_paadSamp.rds")
# maf_paadSamp = readRDS("maf_paadSamp.rds")
genes = getGeneSummary(maf_paadSamp)
dim(genes)
oncoplot(maf = maf_paadSamp, genes = c("KRAS","TP53"),showTumorSampleBarcodes = T)
paadKRAS = maf_paadSamp@data %>% filter(Hugo_Symbol == "KRAS") %>% select(Hugo_Symbol,Variant_Classification, Tumor_Sample_Barcode, HGVSc, HGVSp, HGVSp_Short ) #
paadKRAS$ParticipantBarcode = substr(paadKRAS$Tumor_Sample_Barcode,1,12)
paadKRAS$HGVSp_Short = gsub("p.","", paadKRAS$HGVSp_Short, fixed = T)
# samples with KRAS mutation and common AA sequences
paadKrasSamp = unique(paadKRAS$Tumor_Sample_Barcode)
length(paadKrasSamp)
table(paadKRAS$HGVSp_Short)
# subset TCGA data to those samples
tcgaPaadKras = commonSamp_2MM %>% filter(SampleBarcode %in% substr(paadKrasSamp,1,16))
tab = table(tcgaPaadKras$ParticipantBarcode)
# add mutations to the table to plot
mm = match(names(tab), paadKRAS$ParticipantBarcode)
#barplot(tab)
# add mutation info
dat = data.frame(tab, mut = paadKRAS[mm,"HGVSp_Short"])
rownames(dat) = dat[,1]
colnames(dat) = c("TCGA_barcode","nCDR3","KRAS_mutation")
# add second mutations
p = which(duplicated(paadKRAS$ParticipantBarcode))
s = unlist(paadKRAS[p,"ParticipantBarcode"])
# there are two patients with duplicated mutations
# the first is two different mutations in KRAS
# the second patient has the same mutations in primary and metastatic sample
# add to the first patient only
dat[s[1],"KRAS_mutation"] = paste(dat[s[1],"KRAS_mutation"], paadKRAS[p[1],"HGVSp_Short"], sep = "/")
dat = dat[order(dat$nCDR3, decreasing = T),]
# write this data as a table
write.csv(dat, file = "TCGA_nCDR3.csv", row.names = F)
mutCol = c(G12C = "#7F32BD", G12D = "#5770FF", G12R = "#690002",
G12V = "#FB8808", "G13C/G12A" = "#CCCCCC", Q61H = "#666666", Q61R = "black")
p = ggplot(dat, aes(y = nCDR3, x = TCGA_barcode, fill = KRAS_mutation))+
geom_bar(stat="identity")+
scale_fill_manual(values = mutCol) +
theme(axis.text.x=element_blank()) +
scale_x_discrete(limits=rownames(dat))+
xlab("TCGA sample") +
ylab("# CDR3 with < 3 mismatches")
print(p)
pdf("TCGA_nCDR3.pdf", height = 5)
print(p)
dev.off()
```
```{r }
sessionInfo()
```