XBSeq test for differential expression

Description

The same method is adopted from DESeq for testing differential expression

Usage

XBSeqTest(XB, condA, condB, pvals_only = FALSE, method = c("NP", "MLE"))

Arguments

Details

Differential expression analysis based on statistical methods proposed for DESeq. Details about the method can be found in DESeq manual page. Two methods can be choosen from for method. 'NP' stands for non-parametric method. 'MLE' stands for maximum liklihood estimation method. 'NP' is generally recommended for experiments with replicates smaller than 5.

Value

A data.frame with following columns:

Author(s)

Yuanhang Liu

References

H. I. Chen, Y. Liu, Y. Zou, Z. Lai, D. Sarkar, Y. Huang, et al., "Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads," BMC Genomics, vol. 16 Suppl 7, p. S14, Jun 11 2015.

See Also

XBSeq, estimateSCV

Examples

   data(ExampleData)
   conditions <- factor(c(rep('C1', 3), rep('C2', 3)))
   XB <- XBSeqDataSet(Observed, Background, conditions)
   XB <- estimateRealCount(XB)
   XB <- estimateSizeFactors(XB)
   XB <- estimateSCV(XB)
   Teststas <- XBSeqTest(XB, levels(conditions)[1L], levels(conditions)[2L])
   str(Teststas)
   

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(XBSeq)
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit


Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

    Welcome to 'XBSeq'.
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/XBSeq/XBSeqTest.Rd_%03d_medium.png", width=480, height=480)
> ### Name: XBSeqTest
> ### Title: XBSeq test for differential expression
> ### Aliases: XBSeqTest
> 
> ### ** Examples
> 
>    data(ExampleData)
>    conditions <- factor(c(rep('C1', 3), rep('C2', 3)))
>    XB <- XBSeqDataSet(Observed, Background, conditions)
>    XB <- estimateRealCount(XB)
>    XB <- estimateSizeFactors(XB)
>    XB <- estimateSCV(XB)
>    Teststas <- XBSeqTest(XB, levels(conditions)[1L], levels(conditions)[2L])
>    str(Teststas)
'data.frame':	22609 obs. of  8 variables:
 $ id            : chr  "0610005C13Rik" "0610007C21Rik" "0610007L01Rik" "0610007P08Rik" ...
 $ baseMean      : num  3027 1862 2850 699 2489 ...
 $ baseMeanA     : num  3173 1912 2956 666 2701 ...
 $ baseMeanB     : num  2880 1812 2744 733 2278 ...
 $ foldChange    : num  0.908 0.948 0.929 1.101 0.843 ...
 $ log2FoldChange: num  -0.1396 -0.0776 -0.107 0.1384 -0.246 ...
 $ pval          : num  0.565 0.683 0.714 0.548 0.214 ...
 $ padj          : num  1 1 1 1 1 1 1 1 1 1 ...
>    
> 
> 
> 
> 
> dev.off()
null device 
          1 
>