readInterestResults: Read interest/interest.sequential results text files
In IntEREst: Intron-Exon Retention Estimator

Description Usage Arguments Value Author(s) See Also Examples

Reads one or multiple text file results generated by the interest or interest.sequential functions and builds an object of SummarizedExperiment-class class.

readInterestResults(resultFiles, sampleNames, 
	sampleAnnotation, commonColumns, freqCol, scaledRetentionCol,
	scaleLength, scaleFragment, reScale=FALSE, geneIdCol, 
	repeatsTableToFilter=c())

`resultFiles`	Vector of character strings which includes the path to the tab-separated files resulted by the `interest` function.
`sampleNames`	Vector of character strings which includes the name of the samples. It should be the same size as the `resultFiles` parameter.
`sampleAnnotation`	Data frame with the same row number as the size of `resultFiles` and `sampleNames` parameter. The column names represent the annotation names and values in each column represent the annotations of the samples.
`commonColumns`	Columns in the result file which include intron/exon annotations and are common across all files defined in `resultFiles`.
`freqCol`	Column in the result file which include the read counts for introns/exons.
`scaledRetentionCol`	Column in the result file which include the scaled retention values for introns/exons.
`scaleLength`	Logical value, indicating whether the intron/exon retention levels are scaled to the length of the introns/exons. If `reScale` is `TRUE` the scaled retention levels would be rescalculated when reading the data.
`scaleFragment`	Logical value, indicating whether the intron/exon retention levels are scaled to the fragments mapped to the genes. If `reScale` is `TRUE` the scaled retention levels would be rescalculated when reading the data.
`reScale`	Logical value, indicating whether the scaled retention levels would be rescalculated when reading the data. By default it does not calculate and trusts the user to set the `scaleLength` and `scaleFragment` parameters correctly, i.e. as it was set in the `interest()` or `interest.sequential()` analysis.
`geneIdCol`	The number or name of the column in `resultFiles` which represents the gene/transcript names. It would be used for summing up the number of mapped fragments to the genes when scaling the retention levels. It is only used if `reScale` and `scaleFragment` arguments are set `TRUE`.
`repeatsTableToFilter`	A data.frame table with similar stucture to the `reference`. It includes `chr`, `begin`, and `end` columns. If defined, all reads mapped to the described regions would be ingnored and the Intron/exon lengths would be corrected to exclude the to exclude the regions with repetitive DNA sequences. See `getRepeatTable`. It is only used if `reScale` and `scaleLength` arguments are set `TRUE`.

An object of calss SummarizedExperiment-class.

Ali Oghabian

interest, InterestResult.

geneId<- paste("gene", c(rep(1,7), rep(2,7), rep(3,7), rep(4,7)), 
	sep="_")
readCnt1<- sample(1:100, 28)
readCnt2<- sample(1:100, 28)
readCnt3<- sample(1:100, 28)
readCnt4<- sample(1:100, 28)
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]

#Create tmp director
tmpDir=file.path(tempdir(),"InterestResult")
dir.create(tmpDir)

# Build text files similar to files resulted by interest
dfTmp=data.frame( 
		int_ex=rep(c(rep(c("exon","intron"),3),"exon"),4),
		int_ex_num= rep(c(1,1,2,2,3,3,4),4),         
		int_type=rep(c(NA,"U2",NA,"U12",NA,"U2",NA),4),
		strand=rep("*",28),
		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
)

writeDf<-function(df, file){
	write.table(df, file, col.names=TRUE, 
		row.names=FALSE, quote=FALSE, sep='\t')
}

writeDf(dfTmp[, c(1:5,6,10)], paste(tmpDir, "df1.tsv", sep="/"))
writeDf(dfTmp[, c(1:5,7,11)], paste(tmpDir, "df2.tsv", sep="/"))
writeDf(dfTmp[, c(1:5,8,12)], paste(tmpDir, "df3.tsv", sep="/"))
writeDf(dfTmp[, c(1:5,9,13)], paste(tmpDir, "df4.tsv", sep="/"))

# Build object from generated text file results
testObj<-readInterestResults(
	resultFiles=paste(tmpDir, 
		c("df1.tsv", "df2.tsv", "df3.tsv", "df4.tsv"), sep="/"), 
	sampleNames=c("sam1","sam2","sam3","sam4"), 
	sampleAnnotation= data.frame( gender=c("M","M","F","F"),
		health=c("healthy","unhealthy","healthy","unhealthy")), 
	commonColumns=1:5, freqCol=6, scaledRetentionCol=7, 
	scaleLength=FALSE, scaleFragment=TRUE, reScale=FALSE)

#View object
testObj