DEGseq {DEGseq} | R Documentation |
This function is used to identify differentially expressed genes from RNA-seq data. It takes uniquely mapped reads from RNA-seq data for the two samples with a gene annotation as input. So users should map the reads (obtained from sequencing libraries of the samples) to the corresponding genome in advance.
DEGseq(mapResultBatch1, mapResultBatch2, fileFormat="bed", readLength=32, strandInfo=FALSE, refFlat, groupLabel1="group1", groupLabel2="group2", method=c("LRT", "CTR", "FET", "MARS", "MATR", "FC"), pValue=1e-3, zScore=4, qValue=1e-3, foldChange=4, thresholdKind=1, outputDir="none", normalMethod=c("none", "loess", "median"), depthKind=1, replicate1="none", replicate2="none", replicateLabel1="replicate1", replicateLabel2="replicate2")
mapResultBatch1 |
vector containing uniquely mapping result files for technical replicates of sample1 (or replicate1 when |
mapResultBatch2 |
vector containing uniquely mapping result files for technical replicates of sample2 (or replicate2 when |
fileFormat |
file format: |
readLength |
the length of the reads (only used if |
strandInfo |
whether the strand information was retained during the cloning of the cDNAs.
|
refFlat |
gene annotation file in UCSC refFlat format.
|
groupLabel1 |
label of group1 on the plots. |
groupLabel2 |
label of group2 on the plots. |
method |
method to identify differentially expressed genes. Possible methods are:
|
pValue |
pValue threshold (for the methods: |
zScore |
zScore threshold (for the methods: |
qValue |
qValue threshold (for the methods: |
thresholdKind |
the kind of threshold. Possible kinds are:
|
foldChange |
fold change threshold on MA-plot (for the method: |
outputDir |
the output directory. |
normalMethod |
the normalization method: |
depthKind |
|
replicate1 |
files containing uniquely mapped reads obtained from replicate batch1 (only used when |
replicate2 |
files containing uniquely mapped reads obtained from replicate batch2 (only used when |
replicateLabel1 |
label of replicate batch1 on the plots (only used when |
replicateLabel2 |
label of replicate batch2 on the plots (only used when |
Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289-300.
Jiang,H. and Wong,W.H. (2009) Statistical inferences for isoform expression in RNA-seq. Bioinformatics, 25, 1026-1032.
Bloom,J.S. et al. (2009) Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays. BMC Genomics, 10, 221.
Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.
Storey,J.D. and Tibshirani,R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. 100, 9440-9445.
Wang,L.K. and et al. (2010) DEGseq: an R package for identifying differentially expressed genes from RNA-seq data, Bioinformatics 26, 136 - 138.
Yang,Y.H. et al. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research, 30, e15.
DEGexp
,
getGeneExp
,
readGeneExp
,
kidneyChr21.bed
,
liverChr21.bed
,
refFlatChr21
.
kidneyR1L1 <- system.file("extdata", "kidneyChr21.bed.txt", package="DEGseq") liverR1L2 <- system.file("extdata", "liverChr21.bed.txt", package="DEGseq") refFlat <- system.file("extdata", "refFlatChr21.txt", package="DEGseq") mapResultBatch1 <- c(kidneyR1L1) ## only use the data from kidneyR1L1 and liverR1L2 mapResultBatch2 <- c(liverR1L2) outputDir <- file.path(tempdir(), "DEGseqExample") DEGseq(mapResultBatch1, mapResultBatch2, fileFormat="bed", refFlat=refFlat, outputDir=outputDir, method="LRT") cat("outputDir:", outputDir, "\n")