DEGseq {DEGseq} | R Documentation

## DEGseq: Identify Differentially Expressed Genes from RNA-seq data

### Description

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.

### Usage

    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")

### Arguments

`mapResultBatch1` | vector containing uniquely mapping result files for technical replicates of sample1 (or replicate1 when `method="CTR"`).
`mapResultBatch2` | vector containing uniquely mapping result files for technical replicates of sample2 (or replicate2 when `method="CTR"`).
`fileFormat` | file format: `"bed"` or `"eland"`. example of `"bed"` format: `chr12 7 38 readID 2 +` example of `"eland"` format: `readID chr12.fa 7 U2 F` Note: The field separator character is `TAB`. And the files must follow the format as one of the examples.
`readLength` | the length of the reads (only used if `fileFormat="eland"`).
`strandInfo` | whether the strand information was retained during the cloning of the cDNAs. `"TRUE"` : retained, `"FALSE"`: not retained.
`refFlat` | gene annotation file in UCSC refFlat format. See http://genome.ucsc.edu/goldenPath/gbdDescriptionsOld.html#RefFlat.
`groupLabel1` | label of group1 on the plots.
`groupLabel2` | label of group2 on the plots.
`method` | method to identify differentially expressed genes. Possible methods are: `"LRT"`: Likelihood Ratio Test (Marioni et al. 2008), `"CTR"`: Check whether the variation between two Technical Replicates can be explained by the random sampling model (Wang et al. 2009), `"FET"`: Fisher's Exact Test (Joshua et al. 2009), `"MARS"`: MA-plot-based method with Random Sampling model (Wang et al. 2009), `"MATR"`: MA-plot-based method with Technical Replicates (Wang et al. 2009), `"FC"` : Fold-Change threshold on MA-plot.
`pValue` | pValue threshold (for the methods: `LRT, FET, MARS, MATR`). only used when `thresholdKind=1`.
`zScore` | zScore threshold (for the methods: `MARS, MATR`). only used when `thresholdKind=2`.
`qValue` | qValue threshold (for the methods: `LRT, FET, MARS, MATR`). only used when `thresholdKind=3` or `thresholdKind=4`.
`thresholdKind` | the kind of threshold. Possible kinds are: `1`: pValue threshold, `2`: zScore threshold, `3`: qValue threshold (Benjamini et al. 1995), `4`: qValue threshold (Storey et al. 2003), `5`: qValue threshold (Storey et al. 2003) and Fold-Change threshold on MA-plot are both required (can be used only when `method="MARS"`).
`foldChange` | fold change threshold on MA-plot (for the method: `FC`).
`outputDir` | the output directory.
`normalMethod` | the normalization method: `"none", "loess", "median"` (Yang,Y.H. et al. 2002). recommend: `"none"`.
`depthKind` | `1`: take the total number of reads uniquely mapped to genome as the depth for each replicate, `0`: take the total number of reads uniquely mapped to all annotated genes as the depth for each replicate. We recommend taking `depthKind=1`, especially when the genes in annotation file are part of all genes.
`replicate1` | files containing uniquely mapped reads obtained from replicate batch1 (only used when `method="MATR"`).
`replicate2` | files containing uniquely mapped reads obtained from replicate batch2 (only used when `method="MATR"`).
`replicateLabel1` | label of replicate batch1 on the plots (only used when `method="MATR"`).
`replicateLabel2` | label of replicate batch2 on the plots (only used when `method="MATR"`).

### References

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.

### See Also

`DEGexp`, `getGeneExp`, `readGeneExp`, `kidneyChr21.bed`, `liverChr21.bed`, `refFlatChr21`.

### Examples

      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")

[Package DEGseq version 1.44.0 Index]