In piotrsobecki/rdmdecon: Deconvolution based on Roadmap data

knitr::opts_chunk$set(echo = TRUE,error=TRUE,warnings=FALSE)

library(rdmdeconv)

marker extraction based on default proportions

#Make default signatures
signatures <- make_signatures()

#Extract markers from signatures
extracted_markers <- extract_markers(signatures)
extracted_markers <- extracted_markers[extracted_markers$padj==0,] # get only markers with 0 padj (lowest)
str(extracted_markers)
#Show chosen extracted markers
head(data.frame(signatures[extracted_markers$id,],extracted_markers$padj),10)

BRCA.rnaseq data

setup

if( !require(BiocInstaller) ){
    # enable Bioconductor repositories
    # -> add Bioc-software
    setRepositories() 
    source("https://bioconductor.org/biocLite.R")
    biocLite("BiocInstaller")
    #install.packages('BiocInstaller')
    library(BiocInstaller)
}

if (!require(RTCGA.rnaseq)){
  #biocLite("RTCGA")
  biocLite("RTCGA.rnaseq")
  library(RTCGA.rnaseq)
}

dataset = BRCA.rnaseq[1:10,] #subset of patients

Map gene names from BRCA to Roadmap format

mix <- map_genes_brca(dataset,signatures)

Just test if gene mapping was correctly performed

test_gene_mapping(dataset,mix)

57 Signature deconvolution of BRCA.rnaseq data using extracted markers

#Filter data (ex. available genes) performed by deconv anyway
mix <- filter_data(mix,signatures)

#Deconvolution
res <- deconv(mix,signatures,markers=extracted_markers$id)
head(res[,1:6])
plotRes(res)