In vjcitn/tumex5: RNASeq2GeneNorm assays in HDF Scalable Data Service for five TCGA tumors

suppressPackageStartupMessages({
suppressMessages({
library(rhdf5client)
library(tumex5)
library(BiocOncoTK)
library(restfulSE)
})
})

Introduction

We would like to demonstrate how workflows can employ HSDS and (RESTful) SummarizedExperiment instances.

We retrieved five tumors' expression data using curatedTCGAData. These were converted to HDF5 SummarizedExperiment instances, and the HDF5 representations of quantifications were ported to HSDS using hsload.

The following function produces a 'long' dataset for a given tumor type, binding the MSIsensor scores of Ding et al 2018.

 exprByMSI = function(tumcode, genesym, alias) {
  if (missing(alias)) alias=genesym
  ob = paste0(tumcode, "rnagn")
  ex = get(ob)
  ex = BiocOncoTK::bindMSI(ex)
  data.frame(
   patient_barcode=colnames(ex),
   acronym=tumcode,
   symbol = genesym,
   alias = alias,
   log2ex=log2(as.numeric(SummarizedExperiment::assay(ex[genesym,]))+1),
   msicode = ifelse(ex$msiTest >= 4, ">=4", "<4"))
 }
head(exprByMSI("COAD", "CD8A"))

Application

We define a small set of genes indicative of immune infiltration of tumor. We then build a data.frame instance with tumor codes and gene symbols constituting all combinations of tumor type and gene.

infilGenes = c(`PD-L1`="CD274", `PD-L2`="PDCD1LG2", CD8A="CD8A")
tumcodes = c("COAD", "STAD", "UCEC")
combs = expand.grid(tumcode=tumcodes, ali=names(infilGenes),
    stringsAsFactors=FALSE)
combs$sym = infilGenes[combs$ali]

The job is completed by building a long data.frame for all combinations and visualizing.

 allshow = lapply(1:nrow(combs), function(x) exprByMSI(combs$tumcode[x],
    combs$sym[x], combs$ali[x]))

 rr = do.call(rbind, allshow)

 library(ggplot2)
 ggplot(rr,
    aes(msicode, log2ex)) + geom_boxplot() +
    facet_grid(acronym~alias) +
    ylab("log2(normalized expr. + 1)") +
    xlab("microsatellite instability score")

vjcitn/tumex5 documentation built on May 31, 2019, 1:56 a.m.