DEGexp {DEGseq} | R Documentation

## DEGexp: Identifying Differentially Expressed Genes from gene expression data

### Description

This function is used to identify differentially expressed genes when users already have the gene expression values (or the number of reads mapped to each gene).

### Usage

    DEGexp(geneExpMatrix1, geneCol1=1, expCol1=2, depth1=rep(0, length(expCol1)), groupLabel1="group1",
           geneExpMatrix2, geneCol2=1, expCol2=2, depth2=rep(0, length(expCol2)), 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"),
           replicateExpMatrix1=NULL, geneColR1=1, expColR1=2, depthR1=rep(0, length(expColR1)), replicateLabel1="replicate1",
           replicateExpMatrix2=NULL, geneColR2=1, expColR2=2, depthR2=rep(0, length(expColR2)), replicateLabel2="replicate2", rawCount=TRUE)

### Arguments

`geneExpMatrix1` | gene expression matrix for replicates of sample1 (or replicate1 when `method="CTR"`).
`geneCol1` | gene id column in geneExpMatrix1.
`expCol1` | expression value columns in geneExpMatrix1 for replicates of sample1 (numeric vector). Note: Each column corresponds to a replicate of sample1.
`depth1` | the total number of reads uniquely mapped to genome for each replicate of sample1 (numeric vector), default: take the total number of reads mapped to all annotated genes as the depth for each replicate.
`groupLabel1` | label of group1 on the plots.
`geneExpMatrix2` | gene expression matrix for replicates of sample2 (or replicate2 when `method="CTR"`).
`geneCol2` | gene id column in geneExpMatrix2.
`expCol2` | expression value columns in geneExpMatrix2 for replicates of sample2 (numeric vector). Note: Each column corresponds to a replicate of sample2.
`depth2` | the total number of reads uniquely mapped to genome for each replicate of sample2 (numeric vector), default: take the total number of reads mapped to all annotated genes as the depth for each replicate.
`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 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 et al. 2002). recommend: `"none"`.
`replicateExpMatrix1` | matrix containing gene expression values for replicate batch1 (only used when `method="MATR"`). Note: replicate1 and replicate2 are two (groups of) technical replicates of a sample.
`geneColR1` | gene id column in the expression matrix for replicate batch1 (only used when `method="MATR"`).
`expColR1` | expression value columns in the expression matrix for replicate batch1 (numeric vector) (only used when `method="MATR"`).
`depthR1` | the total number of reads uniquely mapped to genome for each replicate in replicate batch1 (numeric vector), default: take the total number of reads mapped to all annotated genes as the depth for each replicate (only used when `method="MATR"`).
`replicateLabel1` | label of replicate batch1 on the plots (only used when `method="MATR"`).
`replicateExpMatrix2` | matrix containing gene expression values for replicate batch2 (only used when `method="MATR"`). Note: replicate1 and replicate2 are two (groups of) technical replicates of a sample.
`geneColR2` | gene id column in the expression matrix for replicate batch2 (only used when `method="MATR"`).
`expColR2` | expression value columns in the expression matrix for replicate batch2 (numeric vector) (only used when `method="MATR"`).
`depthR2` | the total number of reads uniquely mapped to genome for each replicate in replicate batch2 (numeric vector), default: take the total number of reads mapped to all annotated genes as the depth for each replicate (only used when `method="MATR"`).
`replicateLabel2` | label of replicate batch2 on the plots (only used when `method="MATR"`).
`rawCount` | a logical value indicating the gene expression values are based on raw read counts or normalized values.

### 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. (2008) 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

`DEGexp2`, `DEGseq`, `getGeneExp`, `readGeneExp`, `GeneExpExample1000`, `GeneExpExample5000`.

### Examples

      ## kidney: R1L1Kidney, R1L3Kidney, R1L7Kidney, R2L2Kidney, R2L6Kidney
      ## liver: R1L2Liver, R1L4Liver, R1L6Liver, R1L8Liver, R2L3Liver

      geneExpFile <- system.file("extdata", "GeneExpExample5000.txt", package="DEGseq")
      cat("geneExpFile:", geneExpFile, "\n")
      outputDir <- file.path(tempdir(), "DEGexpExample")
      geneExpMatrix1 <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(7,9,12,15,18))
      geneExpMatrix2 <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(8,10,11,13,16))
      geneExpMatrix1[30:32,]
      geneExpMatrix2[30:32,]
      DEGexp(geneExpMatrix1=geneExpMatrix1, geneCol1=1, expCol1=c(2,3,4,5,6), groupLabel1="kidney",
             geneExpMatrix2=geneExpMatrix2, geneCol2=1, expCol2=c(2,3,4,5,6), groupLabel2="liver",
             method="LRT", outputDir=outputDir)
      cat("outputDir:", outputDir, "\n")

[Package DEGseq version 1.41.0 Index]