exactTestInterest {IntEREst} | R Documentation |
Compute genewise exact test between two groups of read counts, using the
edgeR
package.
exactTestInterest(x, sampleAnnoCol=c(), sampleAnnotation=c(), geneIdCol, silent=TRUE, group=c(), rejection.region="doubletail", big.count=900, prior.count=0.125, disp="common", ...)
x |
Object of type |
sampleAnnoCol |
Which colummn of |
sampleAnnotation |
A vector of size 2 which cotains values from |
geneIdCol |
Column name (or number of column) in |
silent |
Whether run the function silently, i.e. without printing the top differential expression tags. |
group |
Vector to manually define the sample groups (or annotations). It is ignored if
|
rejection.region |
The |
big.count |
The |
prior.count |
The |
disp |
The type of estimating the dispersion in the data. Available options are:
"tagwise", "trended", "common" and "genewise". It is also possible to assign a
number for manually setting the |
... |
Other parameter settings for the |
table |
Data frame containing columns for the log2 fold-change (logFC), the average of log2 counts-per-million (logCPM), and the two-sided p-value (PValue). |
comparison |
The name of the two compared groups. |
dispersionType |
The name of the type of dispersion used. |
dispersion |
The estimated dispersion values. |
Ali Oghabian
lfc
, glmInterest
, qlfInterest
,
treatInterest
, DEXSeqIntEREst
geneId<- paste("gene", c(rep(1,5), rep(2,5), rep(3,5), rep(4,5)), sep="_") readCnt1<- sample(1:100, 20) readCnt2<- sample(1:100, 20) readCnt3<- sample(1:100, 20) readCnt4<- sample(1:100, 20) fpkm1<- readCnt1/(tapply(readCnt1, geneId, sum))[geneId] fpkm2<- readCnt2/(tapply(readCnt2, geneId, sum))[geneId] fpkm3<- readCnt3/(tapply(readCnt3, geneId, sum))[geneId] fpkm4<- readCnt4/(tapply(readCnt4, geneId, sum))[geneId] # Creating object using test data interestDat<- data.frame( int_ex=rep(c(rep(c("exon","intron"),2),"exon"),4), int_ex_num= rep(c(1,1,2,2,3),4), gene_id= geneId, sam1_readCnt=readCnt1, sam2_readCnt=readCnt2, sam3_readCnt=readCnt3, sam4_readCnt=readCnt4, sam1_fpkm=fpkm1, sam2_fpkm=fpkm2, sam3_fpkm=fpkm3, sam4_fpkm=fpkm4 ) readFreqColIndex<- grep("_readCnt$",colnames(interestDat)) scaledRetentionColIndex<- grep("_fpkm$",colnames(interestDat)) scalRetTmp<- as.matrix(interestDat[ ,scaledRetentionColIndex]) colnames(scalRetTmp)<-gsub("_fpkm$","", colnames(scalRetTmp)) frqTmp<- as.matrix(interestDat[ ,readFreqColIndex]) colnames(frqTmp)<-gsub("_readCnt$","", colnames(frqTmp)) InterestResultObj<- InterestResult( resultFiles=paste("file",1:4, sep="_"), rowData= interestDat[ , -c(readFreqColIndex, scaledRetentionColIndex)], counts= frqTmp, scaledRetention= scalRetTmp, scaleLength=TRUE, scaleFragment=FALSE, sampleAnnotation=data.frame( sampleName=paste("sam",1:4, sep=""), gender=c("M","M","F","F"), row.names=paste("sam", 1:4, sep="") ) ) res<- exactTestInterest(InterestResultObj, sampleAnnoCol="gender", sampleAnnotation=c("F","M"), geneIdCol= "gene_id", silent=TRUE, disp="common")