BitSeq-package {BitSeq} | R Documentation

## Bayesian Inference of Transcripts from Sequencing data

### Description

The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process.

In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments.

The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression.

### Author(s)

Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Peter Glaus <glaus@cs.man.ac.uk>

### References

Glaus, P., Honkela, A. and Rattray, M. (2012). Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 28(13), 1721-1728.

### Examples

    ## Not run:
    ## basic use
    res1 <- getExpression("data-c0b0.sam","ensSelect1.fasta")
    res2 <- getExpression("data-c0b1.sam","ensSelect1.fasta")
    res3 <- getExpression("data-c1b0.sam","ensSelect1.fasta")
    res4 <- getExpression("data-c1b1.sam","ensSelect1.fasta")

    deRes <- getDE( list(c(res1$fn, res2$fn),
                         c(res3$fn, res4$fn)) )
    ## top 10 differentially expressed
    head(deRes$pplr[ order(abs(0.5-deRes$pplr$pplr), decreasing=TRUE ), ], 10)

    ## advanced use, keeping the intermediate files
    parseAlignment( "data-c0b0.sam",
       outFile = "data-c0b0.prob",
       trSeqFile = "ensSelect1.fasta",
       trInfoFile = "data.tr",
       uniform = TRUE,
       verbose = TRUE )

    estimateExpression( "data-c0b0.prob",
       outFile = "data-c0b0",
       outputType = "RPKM",
       trInfoFile = "data.tr",
       MCMC_burnIn = 200,
       MCMC_samplesN = 200,
       MCMC_samplesSave = 100,
       MCMC_scaleReduction = 1.1,
       MCMC_chainsN = 2 )

    cond1Files = c("data-c0b0.rpkm","data-c0b1.rpkm")
    cond2Files = c("data-c1b1.rpkm","data-c1b1.rpkm")
    allConditions = list(cond1Files, cond2Files)

    getMeanVariance( allConditions,
       outFile = "data.means",
       log = TRUE )

    estimateHyperPar( allConditions,
       outFile = "data.par",
       meanFile = "data.means",
       verbose = TRUE )

    estimateDE( allConditions,
       outFile = "data",
       parFile = "data.par" )

    ## End(Not run)

[Package BitSeq version 1.24.0 Index]