Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), < http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
An R package for Differential Expression Analysis. Given count data from two experimental conditions, denoiSeq helps one determine which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification developed in "Chromatin conformation governs T cell receptor J beta gene segment usage", by Ndifon et al.
To use the package, one needs to create a
readsData object and invoke the
denoiseq function on it. The results are obtained from the return value of denoiseq using the
results function which then computes the test statistic used in differential analysis.
RD <- new("readsData", counts = ERCC) #creating the readsData object steps <- 3000 #steps for MCMC BI <- denoiseq(RD, steps) #invoking denoiseq on the readsData object rez <- results(BI,steps) #computing the test statistic
This package can be istalled from CRAN using install.packages("denoiSeq") or from github using devtools::install_github("buriom/denoiSeq").
This version of denoiSeq fixes Errors with vignette building.