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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.

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Modeling expression ranks for noise-tolerant differential expressionanalysis of scRNA-Seq data

Introduction

ROSeq - A rank based approach to modeling gene expression with filteredand normalized read count matrix. ROSeq takes filtered and normalizedread matrix and cell-annotation/condition as input and determines thedifferentially expressed genes between the contrasting groups of singlecells. One of the input parameters is the number of cores to be used.

Installation

The developer’s version of the R package can be installed with thefollowing R commands:

if (!requireNamespace("BiocManager",quietly=TRUE))    install.packages("BiocManager")# The following initializes usage of Bioc develBiocManager::install(version='devel')BiocManager::install("ROSeq")

The github’s version of the R package can be installed with thefollowing R commands:

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

Vignette tutorial

This vignette uses the Tung dataset, which is already inbuilt in thepackage, to demonstrate a standard pipeline.

Example

Libraries need to be loaded before running.

library(ROSeq)library(edgeR)#> Loading required package: limmalibrary(limma)

Loading tung dataset

samples<-list()samples$count<-ROSeq::L_Tung_single$NA19098_NA19101_countsamples$group<-ROSeq::L_Tung_single$NA19098_NA19101_groupsamples$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

Below commands can be used for Cell/gene filtering, TMM normalizationand voom transformation. The user is free to use an alternativepreprocessing strategy while using different filtering/normalizationmethods.

gene_names<-rownames(samples$count)samples$count<-apply(samples$count,2,function(x) as.numeric(x))rownames(samples$count)<-gene_namessamples$count<-samples$count[,colSums(samples$count>0)>2000]gkeep<-apply(samples$count,1,function(x) sum(x>2)>=3)samples$count<-samples$count[gkeep,]samples$count<-limma::voom(ROSeq::TMMnormalization(samples$count))

ROSeq analysis.

Input: gene expression matrix with genes in rows and cells in columns.Condition/group annotation of cells also need to be supplied. User canset numCores based the hardware specifications in hercomputer.

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

Showing results are in the form of pVals and pAdj

p_Vals : p_value (unadjusted)
p_Adj : Adjusted p-value, based on FDR method
output[1:5,]#>                     pVals      pAdj#> ENSG00000237683 0.6741425 0.9321651#> ENSG00000188976 0.7484244 0.9426495#> ENSG00000187608 0.2282451 0.8481636#> ENSG00000188157 0.5138812 0.9082800#> ENSG00000131591 0.1235577 0.7438811

About

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.

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