exactTestInterest: Exact test

In gacatag/IntEREst: Intron-Exon Retention Estimator

Exact test

Description

Compute genewise exact test between two groups of read counts, using the edgeR package.

Usage

exactTestInterest(x, sampleAnnoCol=c(), sampleAnnotation=c(), 
	geneIdCol, silent=TRUE, group=c(), rejection.region="doubletail", 
	big.count=900, prior.count=0.125, disp="common", ...)

Arguments

x	Object of type SummarizedExperiment.
sampleAnnoCol	Which colummn of colData of x to consider for the analysis.
sampleAnnotation	A vector of size 2 which cotains values from colData of SummarizedExperiment object; e.g. if getAnnotation(x)[, sampleAnnoCol]= c("test", "test", "ctrl","ctrl", ...) , and the goal is to compare "test" and "ctrl" samples, sampleAnnotation should either be c("test", "ctrl") or c("ctrl", "test").
geneIdCol	Column name (or number of column) in rowData of x, i.e. SummarizedExperiment object, that represents the gene ID of the introns and exons in x.
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 sampleAnnopCol is defined.
rejection.region	The rejection.region parameter in exactTest from edgeR package.
big.count	The big.count parameter in exactTest from edgeR package.
prior.count	The prior.count parameter in exactTest from edgeR package.
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 disp.
...	Other parameter settings for the estimateDisp function (e.g. the design parameter) in the edgeR package.

Value

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.

Author(s)

Ali Oghabian

See Also

lfc, glmInterest, qlfInterest, treatInterest, DEXSeqIntEREst

Examples

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")

gacatag/IntEREst documentation built on July 29, 2024, 1:12 a.m.