DEGexp {DEGseq} | R Documentation |
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 |
geneCol1 |
gene id column in geneExpMatrix1. |
expCol1 |
expression value columns in geneExpMatrix1 for replicates of sample1 (numeric vector).
|
depth1 |
the total number of reads uniquely mapped to genome for each replicate of sample1 (numeric vector),
|
groupLabel1 |
label of group1 on the plots. |
geneExpMatrix2 |
gene expression matrix for replicates of sample2 (or replicate2 when |
geneCol2 |
gene id column in geneExpMatrix2. |
expCol2 |
expression value columns in geneExpMatrix2 for replicates of sample2 (numeric vector).
|
depth2 |
the total number of reads uniquely mapped to genome for each replicate of sample2 (numeric vector),
|
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: |
replicateExpMatrix1 |
matrix containing gene expression values for replicate batch1 (only used when |
geneColR1 |
gene id column in the expression matrix for replicate batch1 (only used when |
expColR1 |
expression value columns in the expression matrix for replicate batch1 (numeric vector) (only used when |
depthR1 |
the total number of reads uniquely mapped to genome for each replicate in replicate batch1 (numeric vector),
|
replicateLabel1 |
label of replicate batch1 on the plots (only used when |
replicateExpMatrix2 |
matrix containing gene expression values for replicate batch2 (only used when |
geneColR2 |
gene id column in the expression matrix for replicate batch2 (only used when |
expColR2 |
expression value columns in the expression matrix for replicate batch2 (numeric vector) (only used when |
depthR2 |
the total number of reads uniquely mapped to genome for each replicate in replicate batch2 (numeric vector),
|
replicateLabel2 |
label of replicate batch2 on the plots (only used when |
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")