In ntyet/rmRNAseq: RNA-Seq Analysis for Repeated-Measures Data

knitr::opts_chunk$set(
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rmRNAseq

The goal of rmRNAseq is to conduct differential expression analysis using RNA-seq data from repeated-measures designs, where mRNA samples are obtained repeatedly at different times from same experimental units. Our method is developed based on a general linear model framework with continuous autoregressive correlation structure of order one, accompanied by a parametric bootstrap inference strategy to conduct general hypothesis testings.

Installation

The package can be installed from GitHub with:

# install.packages("devtools")
devtools::install_github("ntyet/rmRNAseq")

Example

This is a basic example which shows you how to solve a common problem:

library(rmRNAseq)
data(dat)
data(design)
data(covset)
Subject <- covset$ear # identity of experimental units
Time <- covset$time # times at which mRNA samples are taken
Nboot <- 2  # for real data analysis, use Nboot at least 100
ncores <- 1 # for real data analysis and if the computer allows, increase ncores to save time
print.progress <- FALSE
saveboot <- FALSE
circadian <- TRUE  #  FALSE if the experiment does not show circadian rhythm effect
counts <- dat[1:3,]
C.matrix <- list()
# test for Line main effect
C.matrix[[1]] <- limma::makeContrasts(line2, levels = design)
# test for Time main effect
C.matrix[[2]] <- limma::makeContrasts(time2, time6, time24, levels = design)
names(C.matrix) <- c("line2", "time")
TCout <- rmRNAseq:::TC_CAR1(counts, design, Subject, Time, C.matrix,
Nboot, ncores, print.progress, saveboot, circadian)
names(TCout)
TCout$NewTime[1:4]
TCout$pqvalue$pv
TCout$pqvalue$qv