DEGexp: DEGexp: Identifying Differentially Expressed Genes from gene...
In DEGseq: Identify Differentially Expressed Genes from RNA-seq data

Description Usage Arguments References See Also Examples

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).

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)

`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.

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.

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

  ## 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")

Loading required package: qvalue
Loading required package: samr
Loading required package: impute
Loading required package: matrixStats
geneExpFile: /usr/local/lib/R/site-library/DEGseq/extdata/GeneExpExample5000.txt 
                EnsemblGeneID     R1L1Kidney R1L3Kidney R1L7Kidney R2L2Kidney
ENSG00000188976 "ENSG00000188976" "73"       "77"       "68"       "70"      
ENSG00000187961 "ENSG00000187961" "15"       "15"       "13"       "12"      
ENSG00000187583 "ENSG00000187583" "1"        "1"        "3"        "0"       
                R2L6Kidney
ENSG00000188976 "82"      
ENSG00000187961 "15"      
ENSG00000187583 "3"       
                EnsemblGeneID     R1L2Liver R1L4Liver R1L6Liver R1L8Liver
ENSG00000188976 "ENSG00000188976" "34"      "56"      "45"      "55"     
ENSG00000187961 "ENSG00000187961" "8"       "13"      "11"      "12"     
ENSG00000187583 "ENSG00000187583" "0"       "1"       "0"       "0"      
                R2L3Liver
ENSG00000188976 "42"     
ENSG00000187961 "20"     
ENSG00000187583 "2"      
Please wait...
gene id column in geneExpMatrix1 for sample1:  1 
expression value column(s) in geneExpMatrix1: 2 3 4 5 6 
total number of reads uniquely mapped to genome obtained from sample1: 345504 354981 334557 366041 372895 
gene id column in geneExpMatrix2 for sample2:  1 
expression value column(s) in geneExpMatrix2: 2 3 4 5 6 
total number of reads uniquely mapped to genome obtained from sample2: 274430 274486 264999 255041 284205 

method to identify differentially expressed genes:  LRT 
pValue threshold: 0.001 
output directory: /work/tmp/tmp/RtmpIDAtzY/DEGexpExample 

Please wait ...
Identifying differentially expressed genes ...
Please wait patiently ...
output ...

Done ...
The results can be observed in directory:  /work/tmp/tmp/RtmpIDAtzY/DEGexpExample 
outputDir: /work/tmp/tmp/RtmpIDAtzY/DEGexpExample