ROSeq - A rank based approach to modeling gene expression

Author: Krishan Gupta

Introduction

ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.

Installation

The developer version of the R package can be installed with the following R commands:

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install('ROSeq')

or can be installed with the following R commands:

library(devtools)
install_github('krishan57gupta/ROSeq')

Vignette tutorial

This vignette uses a tung dataset already inbuilt in same package, to demonstrate a standard pipeline. This vignette can be used as a tutorial as well. Ref: Tung, P.-Y.et al.Batch effects and the effective design of single-cell geneexpression studies.Scientific reports7, 39921 (2017).

Example

Libraries need to be loaded before running.

library(ROSeq)
library(edgeR)
library(limma)

samples<-list()
samples$count<-ROSeq::L_Tung_single$NA19098_NA19101_count
samples$group<-ROSeq::L_Tung_single$NA19098_NA19101_group
samples$count[1:5,1:5] #> NA19098.r1.A01 NA19098.r1.A02 NA19098.r1.A03 NA19098.r1.A04 #> ENSG00000237683 0 0 0 1 #> ENSG00000187634 0 0 0 0 #> ENSG00000188976 3 6 1 3 #> ENSG00000187961 0 0 0 0 #> ENSG00000187583 0 0 0 0 #> NA19098.r1.A05 #> ENSG00000237683 0 #> ENSG00000187634 0 #> ENSG00000188976 4 #> ENSG00000187961 0 #> ENSG00000187583 0 Data Preprocessing: cells and genes filtering then voom transformation after TMM normalization samples$count=apply(samples$count,2,function(x) as.numeric(x)) gkeep <- apply(samples$count,1,function(x) sum(x>0)>5)
samples$count<-samples$count[gkeep,]
samples$count<-limma::voom(ROSeq::TMMnormalization(samples$count))

ROSeq calling

output<-ROSeq(countData=samples$count, condition = samples$group, numCores=1)

Showing results are in the form of pval, padj and log2FC

output[1:5,]
#> [5,] 0.0077068770 0.045497442 -0.05582549