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06_TPM_normalized_Data.Rmd
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---
title: "Visualize TPM Normalized TCGA and GTEx Data"
author: "Sonali Arora, Hamid Bolouri"
date: "October 1, 2018"
output:
html_document:
toc: true
theme: united
---
## Introduction
In this vignette, we read in the processed SE objects, and compute Principal
Components using log2(TPM+0.001) gene expression. We then proceed to save the
Principal Components which we use in later vignettes for plotting.
## TPM Normalized TCGA Data
```{r eval=FALSE}
rm(list=ls())
library(SummarizedExperiment)
library(Hmisc)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data
results_dir = file.path(bigdir, "data")
if(!file.exists( file.path(s3_dir, "SE_objects"))){
stop("Please go through vignette 3 & 4 to make SE objects or download from S3 bucket")
}
if(!file.exists( file.path( results_dir))){
system(paste0("mkdir ", results_dir))
}
if(!file.exists( file.path( results_dir, "pca_data"))){
system(paste0("mkdir ", file.path(results_dir, "pca_data")))
}
maindir = file.path(results_dir, "pca_data")
tcga_gdc <- get(load( file.path( s3_dir, "SE_objects","tcga_gdc_log2_TPM.RData")))
tcga_mskcc_norm <- get(load( file.path( s3_dir, "SE_objects",
"tcga_mskcc_norm_log2_TPM.RData")))
tcga_mskcc_batch <- get(load( file.path( s3_dir, "SE_objects",
"tcga_mskcc_batch_log2_TPM.RData")))
tcga_recount2 <- get(load( file.path( s3_dir, "SE_objects",
"tcga_recount2_log2_TPM.RData")))
tcga_xena <- get(load( file.path( s3_dir, "SE_objects",
"tcga_xena_log2_TPM.RData")))
tcga_piccolo <- get(load( file.path( s3_dir, "SE_objects",
"tcga_piccolo_log2_TPM.RData")))
gdc_mat = assay(tcga_gdc)
mskcc_norm_mat=assay(tcga_mskcc_norm)
mskcc_batch_mat=assay(tcga_mskcc_batch)
piccolo_mat=assay(tcga_piccolo)
recount2_mat=assay(tcga_recount2)
xena_mat= assay(tcga_xena)
myFun = function(gdc_mat, other_mat , resdir,
other_title, other_color)
{
message("GDC_vs_", other_title)
message("Start Time = ", Sys.time())
pca_pdf = file.path(resdir,
paste0("Supp_Fig1_PCA_Data_GDC_vs_",other_title,"_TPM.pdf"))
pca_data_file = file.path(resdir,
paste0("Supp_Fig1_PCA_Data_GDC_vs_",other_title,"_TPM.txt"))
message("Computing PCA...")
final1=cbind(gdc_mat, other_mat)
pc1= prcomp(t(final1))
message("PCA computed .. begin writing files")
percentVar <- (pc1$sdev^2 / sum( pc1$sdev^2 ) )*100
percentVar= round(percentVar[1:2], 2)
message(" pc1:", percentVar[1])
message(" pc2:", percentVar[2])
#save(pc1, file = file.path(resdir, paste0("pca_object_GDC_vs_",other_title,"_TPM.RData")))
write.table(percentVar,
file.path(resdir, paste0("percentVar_GDC_vs_",other_title,"_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
coldata1= data.frame(Project = c(rep("TCGA-GDCData",ncol(gdc_mat)),
rep(other_title, ncol(gdc_mat))))
pc_data1 = data.frame(PC1=pc1$x[,1], PC2=pc1$x[,2],
Project = coldata1[,"Project"],
sampleName = colnames(final1))
write.table(pc_data1, pca_data_file,
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
message("Files written successfully! ")
message("End Time: ", Sys.time())
message("##############################")
}
myFun(gdc_mat, other_mat=xena_mat , resdir= maindir,
other_title="XENA_Toil", other_color="orange")
myFun(gdc_mat, other_mat=piccolo_mat , resdir= maindir,
other_title="Piccolo_Lab", other_color="#E76BF3")
myFun(gdc_mat, other_mat=recount2_mat , resdir= maindir,
other_title="Recount2", other_color="grey45")
myFun(gdc_mat, other_mat=mskcc_norm_mat , resdir= maindir,
other_title="MSKCC_Norm", other_color="darkblue")
myFun(gdc_mat, other_mat=mskcc_batch_mat , resdir= maindir,
other_title="MSKCC_BATCH", other_color="#00B0F6")
# all datasets together
message("PCA for all datasets .. starting .. takes a while")
final_all = cbind(gdc_mat, piccolo_mat, mskcc_norm_mat, mskcc_batch_mat,
recount2_mat, xena_mat)
tcga_tpm= prcomp(t(final_all))
percentVar <- (tcga_tpm$sdev^2 / sum(tcga_tpm$sdev^2 ) )*100
percentVar= round(percentVar[1:2], 2)
# percentVar
# save(tcga_tpm, file = file.path(maindir, "pca_object_TCGA_all_dataset_TPM.RData"))
write.table(percentVar,
file.path(maindir, "percentVar_TCGA_all_datasets_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
coldata_all = data.frame(Project = c(rep("GDC",ncol(gdc_mat)),
rep("Piccolo_Lab", ncol(gdc_mat)),
rep("MSKCC_Norma", ncol(mskcc_norm_mat)),
rep("MSKCC_BATCH", ncol(mskcc_batch_mat)),
rep("Recount2",ncol(mskcc_batch_mat)),
rep("XENA_Toil", ncol(xena_mat)) ))
pc_data_all = data.frame(PC1=tcga_tpm$x[,1], PC2=tcga_tpm$x[,2],
Project = coldata_all[,"Project"],
sampleName = colnames(final_all))
write.table(pc_data_all,
file.path(maindir, "Fig1_PCA_Data_TCGA_all_datasets_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
# clear all objects from memory
rm(list=ls())
gc()
```
## TPM Normalized GTEx Data
```{r eval=FALSE}
rm(list=ls())
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(Hmisc)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
})
# folder where S3BUCKET data and github directory are stored. eg: ~/Downloads
bigdir = dirname(getwd())
# github directory eg: ~/Downloads/UncertaintyRNA
git_dir = file.path(bigdir, "UncertaintyRNA")
# S3 bucket directory eg: ~/Downloads/OriginalTCGAGTExData
s3_dir = file.path(bigdir, "OriginalTCGAGTExData")
# when you run our RMD files, all results will be stored here.
# This will essentially remake the "data" subfolder from github repo.
# eg:~/Downloads/data
results_dir = file.path(bigdir, "data")
if(!file.exists( s3_dir, "SE_objects")){
stop("Please go through vignette 3 & 4 to make SE objects or download from S3 bucket")
}
if(!file.exists( file.path( results_dir))){
system(paste0("mkdir ", results_dir))
}
if(!file.exists( file.path( results_dir, "pca_data"))){
system(paste0("mkdir ", file.path(results_dir, "pca_data")))
}
maindir = file.path(results_dir, "pca_data")
gtex_v6 <- get(load( file.path( s3_dir, "SE_objects","gtex_v6_log2_TPM.RData")))
gtex_mskcc_norm <- get(load( file.path( s3_dir, "SE_objects",
"gtex_mskcc_norm_log2_TPM.RData")))
gtex_mskcc_batch <- get(load( file.path( s3_dir, "SE_objects",
"gtex_mskcc_batch_log2_TPM.RData")))
gtex_recount2 <- get(load( file.path( s3_dir, "SE_objects",
"gtex_recount2_log2_TPM.RData")))
gtex_xena <- get(load( file.path( s3_dir, "SE_objects",
"gtex_xena_log2_TPM.RData")))
gtex_v6_mat = assay(gtex_v6)
mskcc_norm_mat=assay(gtex_mskcc_norm)
mskcc_batch_mat=assay(gtex_mskcc_batch)
recount2_mat=assay(gtex_recount2)
xena_mat= assay(gtex_xena)
myFun = function(gtex_mat, other_mat , resdir,
other_title, other_color)
{
message("Gtex_v6_vs_", other_title)
message("Start Time = ", Sys.time())
rotation_file = file.path(resdir, paste0("Supp_Fig1_Rotation_Gtex_v6_vs_",other_title,"_TPM.txt"))
pca_pdf = file.path(resdir, paste0("Supp_Fig1_PCA_Data_Gtex_v6_vs_",other_title,"_TPM.pdf"))
pca_data_file = file.path(resdir, paste0("Supp_Fig1_PCA_Data_Gtex_v6_vs_",other_title,"_TPM.txt"))
message("Computing PCA...")
final1=cbind(gtex_mat, other_mat)
pc1= prcomp(t(final1))
message("PCA computed .. begin writing files")
percentVar <- (pc1$sdev^2 / sum( pc1$sdev^2 ) )*100
percentVar= round(percentVar[1:2], 2)
message(" pc1:", percentVar[1])
message(" pc2:", percentVar[2])
#save(pc1, file = file.path(resdir, paste0("pca_object_Gtex_vs_",other_title,"_TPM.RData")))
write.table(percentVar,
file.path(resdir, paste0("percentVar_Gtex_vs_",other_title,"_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
coldata1= data.frame(Project = c(rep("GTEX-V6",ncol(gtex_mat)),
rep(other_title, ncol(gtex_mat))))
pc_data1 = data.frame(PC1=pc1$x[,1], PC2=pc1$x[,2],
Project = coldata1[,"Project"],
sampleName = colnames(final1))
write.table(pc_data1, pca_data_file,
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
message("Files written successfully! ")
message("End Time: ", Sys.time())
message("##############################")
}
# individual datasets
myFun(gtex_v6_mat, other_mat=xena_mat , resdir= maindir,
other_title="XENA_Toil", other_color="orange")
myFun(gtex_v6_mat, other_mat=recount2_mat , resdir= maindir,
other_title="Recount2", other_color="grey")
myFun(gtex_v6_mat, other_mat=mskcc_norm_mat , resdir= maindir,
other_title="MSKCC_Norm", other_color="darkblue")
myFun(gtex_v6_mat, other_mat=mskcc_batch_mat , resdir= maindir,
other_title="MSKCC_BATCH", other_color="#00B0F6")
# all datasets
message("PCA for all datasets starting.. ")
final_all = cbind(gtex_v6_mat, mskcc_norm_mat, mskcc_batch_mat,
recount2_mat, xena_mat)
gtex_tpm= prcomp(t(final_all))
percentVar <- (gtex_tpm$sdev^2 / sum(gtex_tpm$sdev^2 ) )*100
percentVar= round(percentVar[1:2], 2)
# percentVar
#save(gtex_tpm, file = file.path(maindir, "pca_object_GTEX_all_dataset_TPM.RData"))
write.table(percentVar,
file.path(maindir, "percentVar_GTEX_all_datasets_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=FALSE)
coldata_all = data.frame(Project = c(rep("GTEX-V6",ncol(gtex_v6_mat)),
rep("MSKCC_Norm", ncol(mskcc_norm_mat)),
rep("MSKCC_BATCH", ncol(mskcc_batch_mat)),
rep("Recount2",ncol(recount2_mat)),
rep("XENA_Toil", ncol(xena_mat)) ))
pc_data_all = data.frame(PC1=gtex_tpm$x[,1], PC2=gtex_tpm$x[,2],
Project = coldata_all[,"Project"],
sampleName = colnames(final_all))
write.table(pc_data_all,
file.path(maindir, "Fig1_PCA_Data_GTEX_all_datasets_TPM.txt"),
sep="\t", quote=FALSE, row.names=FALSE, col.names=TRUE)
#clear all objects from memory
rm(list=ls())
gc()
```