Package 'DEGseq'

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")

---

DEGexp2: Identifying Differentially Expressed Genes from gene expression data

Description

This function is another (old) version of DEGexp. It takes the gene expression files as input instead of gene expression matrixs.

Usage

DEGexp2(geneExpFile1, geneCol1=1, expCol1=2, depth1=rep(0, length(expCol1)), groupLabel1="group1",
        geneExpFile2, geneCol2=1, expCol2=2, depth2=rep(0, length(expCol2)), groupLabel2="group2",
        header=TRUE, sep="", 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"),
        replicate1="none", geneColR1=1, expColR1=2, depthR1=rep(0, length(expColR1)), replicateLabel1="replicate1",
        replicate2="none", geneColR2=1, expColR2=2, depthR2=rep(0, length(expColR2)), replicateLabel2="replicate2", rawCount=TRUE)

Arguments

geneExpFile1	file containing gene expression values for replicates of sample1 (or replicate1 when method="CTR").
geneCol1	gene id column in geneExpFile1.
expCol1	expression value columns in geneExpFile1 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.
geneExpFile2	file containing gene expression values for replicates of sample2 (or replicate2 when method="CTR").
geneCol2	gene id column in geneExpFile2.
expCol2	expression value columns in geneExpFile2 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.
header	a logical value indicating whether geneExpFile1 and geneExpFile2 contain the names of the variables as its first line. See ?read.table.
sep	the field separator character. If sep = "" (the default for read.table) the separator is white space, that is one or more spaces, tabs, newlines or carriage returns. See ?read.table.
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".
replicate1	file 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 file for replicate batch1 (only used when method="MATR").
expColR1	expression value columns in the expression file 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").
replicate2	file 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 file for replicate batch2 (only used when method="MATR").
expColR2	expression value columns in the expression file 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

DEGexp, 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")
  outputDir <- file.path(tempdir(), "DEGexpExample")
  exp <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(7,9,12,15,18))
  exp[30:35,]
  exp <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(8,10,11,13,16))
  exp[30:35,]
  DEGexp2(geneExpFile1=geneExpFile, geneCol1=1, expCol1=c(7,9,12,15,18), groupLabel1="kidney",
          geneExpFile2=geneExpFile, geneCol2=1, expCol2=c(8,10,11,13,16), groupLabel2="liver",
          method="MARS", outputDir=outputDir)
  cat("outputDir:", outputDir, "\n")

---

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")

---

GeneExpExample1000

Description

GeneExpExample1000.txt includes the first 1000 lines in SupplementaryTable2.txt which is a supplementary file for Marioni,J.C. et al. (2008) (http://genome.cshlp.org/content/18/9/1509/suppl/DC1).

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, getGeneExp, readGeneExp, GeneExpExample5000.

---

GeneExpExample5000

Description

GeneExpExample5000.txt includes the first 5000 lines in SupplementaryTable2.txt which is a supplementary file for Marioni,J.C. et al. (2008) (http://genome.cshlp.org/content/18/9/1509/suppl/DC1).

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, getGeneExp, readGeneExp, GeneExpExample1000.

---

getGeneExp: Count the number of reads and calculate the RPKM for each gene

Description

This function is used to count the number of reads and calculate the RPKM for each gene. It takes uniquely mapped reads from RNA-seq data for a sample with a gene annotation file as input. So users should map the reads (obtained from sequencing library of the sample) to the corresponding genome in advance.

Usage

getGeneExp(mapResultBatch, fileFormat="bed", readLength=32, strandInfo=FALSE,
           refFlat, output=paste(mapResultBatch[1],".exp",sep=""), min.overlapPercent=1)

Arguments

mapResultBatch	vector containing uniquely mapping result files for a sample. Note: The sample can have multiple technical replicates.
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.
output	the output file.
min.overlapPercent	the minimum percentage of the overlapping length for a read and an exon over the length of the read itself, for counting this read from the exon. should be <=1. 0: at least 1 bp overlap between a read and an exon.

Note

This function sums up the numbers of reads coming from all exons of a specific gene (according to the known gene annotation) as the gene expression value. The exons may include the 5'-UTR, protein coding region, and 3'-UTR of a gene. All introns are ignored for a gene for the sequenced reads are from the spliced transcript library. If a read falls in an exon (usually, a read is shorter than an exon), the read count for this exon plus 1. If a read is crossing the boundary of an exon, users can tune the parameter min.overlapPercent, which is the minimum percentage of the overlapping length for a read and an exon over the length of the read itself, for counting this read from the exon. The method use the union of all possible exons for calculating the length for each gene.

References

Mortazavi,A. et al. (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods, 5, 621-628.

See Also

DEGexp, DEGseq, readGeneExp, kidneyChr21.bed, liverChr21.bed, refFlatChr21.

Examples

kidneyR1L1 <- system.file("extdata", "kidneyChr21.bed.txt", package="DEGseq")
  refFlat    <- system.file("extdata", "refFlatChr21.txt", package="DEGseq")
  mapResultBatch <- list(kidneyR1L1)
  output <- file.path(tempdir(), "kidneyChr21.bed.exp")
  exp <- getGeneExp(mapResultBatch, refFlat=refFlat, output=output)
  write.table(exp[30:35,], row.names=FALSE)
  cat("output: ", output, "\n")

---

kidneyChr21.bed

Description

The reads uniquely mapped to human chromosome 21 obtained from the kidney sample sequenced in Run 1, Lane 1.

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, DEGseq, getGeneExp, readGeneExp, liverChr21.bed, refFlatChr21.

---

kidneyChr21Bowtie

Description

The reads uniquely mapped to human chromosome 21 obtained from the kidney sample sequenced in Run 1, Lane 1.

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, DEGseq, getGeneExp, readGeneExp, liverChr21.bed, refFlatChr21.

---

liverChr21.bed

Description

The reads uniquely mapped to human chromosome 21 obtained from the liver sample sequenced in Run 1, Lane 2.

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, DEGseq, getGeneExp, readGeneExp, kidneyChr21.bed, refFlatChr21.

---

liverChr21Bowtie

Description

The reads uniquely mapped to human chromosome 21 obtained from the liver sample sequenced in Run 1, Lane 2.

References

Marioni,J.C. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.

See Also

DEGexp, DEGseq, getGeneExp, readGeneExp, kidneyChr21.bed, refFlatChr21.

---

readGeneExp: read gene expression values to a matrix

Description

This method is used to read gene expression values from a file to a matrix in R workspace. So that the matrix can be used as input of other packages, such as edgeR. The input of the method is a file that contains gene expression values.

Usage

readGeneExp(file, geneCol=1, valCol=2, label = NULL, header=TRUE, sep="")

Arguments

file	file containing gene expression values.
geneCol	gene id column in file.
valCol	expression value columns to be read in the file.
label	label for the columns.
header	a logical value indicating whether the file contains the names of the variables as its first line. See ?read.table.
sep	the field separator character. If sep = "" (the default for read.table) the separator is white space, that is one or more spaces, tabs, newlines or carriage returns. See ?read.table.

See Also

getGeneExp, GeneExpExample1000, GeneExpExample5000.

Examples

## If the data files are collected in a zip archive, the following
  ## commands will first extract them to the temporary directory.

  geneExpFile <- system.file("extdata", "GeneExpExample1000.txt", package="DEGseq")
  exp <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(7,9,12,15,18,8,10,11,13,16))
  exp[30:35,]

---

refFlatChr21

Description

The gene annotation file includes the annotations of genes on chromosome 21, and is in UCSC refFlat format. See http://genome.ucsc.edu/goldenPath/gbdDescriptionsOld.html#RefFlat.

See Also

DEGseq, DEGexp, kidneyChr21.bed, liverChr21.bed.

---