In piotrsobecki/rdmdecon: Deconvolution based on Roadmap data

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

BRCA.rnaseq data - deconvolution for 2 signatures - breast / other (custom proportions)

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)
}

Normalize

proportions <- read.delim('raw/all_proportions_breast_other.txt', sep = '\t',row.names=1)


nmul = 1000000
#Make signatures and normalize to 0..1
nsignatures <- normalize_data_by_columns(make_signatures(proportions))

#Make mix data from BRCA and normaize to 0..1
mix <-map_genes_brca(BRCA.rnaseq[1:10,],nsignatures)
nmix <- normalize_data_by_columns(mix)

#Multiply by val and round because deconvolution performs values >1
nmix = round(nmix * nmul);
nsignatures = round(nsignatures * nmul);

#Clear data
nsignatures <- filter_data(nsignatures,nmix)

#Order by rownames
nmix <- nmix[order(rownames(nmix)),]
nsignatures <- nsignatures[order(rownames(nsignatures)),]

#Extract markers
xmarkers <- extract_markers(nsignatures)
sel_xmarkers <- xmarkers[xmarkers$padj<=10^-2& xmarkers$max>=0.95,]


#Show selected markers for breast
breastMarkers = sel_xmarkers[sel_xmarkers$sig=='Breast',]

ref <- data.frame(
          breastMarkers$id,
          apply(nmix[breastMarkers$id,],1,max),
          apply(nmix[breastMarkers$id,],1,min),
          apply(nmix[breastMarkers$id,],1,mean))

colnames(ref) <- c('Gene','Max','Min','Mean')
ref

nsignatures[breastMarkers$id,]

#Perform deconvolution
res <- deconv(nmix,nsignatures,markers=sel_xmarkers$id)
plotRes(res,0)

piotrsobecki/rdmdecon documentation built on May 25, 2019, 7:14 a.m.