logScoreWithControl {RIPSeeker} | R Documentation

## Compute RIPScore based on RIP and control posteriors and test for significance

### Description

Compute the RIPScore using both RIP and control posteriors for each bins, merge and summarize the scores for the merged bins, and finally compute the p-value and adjusted p-value for the summary RIPScore.

### Usage

    logScoreWithControl(nbhGRRIP, nbhGRCTL, padjMethod = "BH", getControlStats = TRUE)

### Arguments

`nbhGRRIP` | `GRanges` object for the RIP library created from `mainSeek` containing the posterior probabilities of the hidden states for each observed read count.
`nbhGRCTL` | An optional arugment as a `GRanges` object for the control library created from `mainSeek` containing the posterior probabilities of the hidden states for each observed read count.
`padjMethod` | Method used to adjust multiple testing performed in `p.adjust` (Default: "BH").
`getControlStats` | Binary indicator to whether return statistics including computed for the control library alone. If TRUE, then all control specific score columns will be reported with a prefix "CTL".

### Details

The RIPScore is compupted in `computeLogOdd` as the log odd ratio of the posterior for the RIP state (z_{i} = 2) over the posterior for the background state (z_{i} = 1) in RIP library subtracted by the log odd ratio computed from the control library. The adjacent bins with hidden states predicted by `nbh_vit` as the enriched state (corresponding to the NB with larger mean) are merged. The RIPSscores are averaged over the merged bins. To assess the statistical significance of the RIPScore for each region, we assume that the RIPScore follows a Gaussian (Normal) distribution with mean and standard deviation estimated using the RIPScores over all of the bins. The rationale is based on the assumption that most of the RIPScores correspond to the background state and together contribute to a stable estimate of the test statistics (TS) and p-value computed using the R built-in function `pnorm`. The p-value is adjusted by `p.adjust` with BH method by default. The same procedure is applied optionally to the control library.

### Value

`GRanges` of merged bins with values slot saved for RIPScore (lodOdd), p-value (pval), adjusted p-value (pvalAdj) for RIP and optionally for control.

### Note

Internal function used by `seekRIP`.

### Author(s)

Yue Li

### See Also

`logScoreWithoutControl, seekRIP, computeLogOdd, scoreMergedBins`

### Examples

    if(interactive()) { # check the example in seekRIP
    # Retrieve system files
    extdata.dir <- system.file("extdata", package="RIPSeeker")

    bamFiles <- list.files(extdata.dir, ".bam$", recursive=TRUE, full.names=TRUE)

    bamFiles <- grep("PRC2", bamFiles, value=TRUE)

    # Parameters setting
    binSize <- 1e5							# use a large fixed bin size for demo only
    multicore <- FALSE					# use multicore
    strandType <- "-"						# set strand type to minus strand

    ################ run main function for HMM inference on all chromosomes ################
    mainSeekOutputRIP <-
        mainSeek(bamFiles=grep(pattern="SRR039214",
        bamFiles, value=TRUE, invert=TRUE),
    		binSize=binSize, strandType=strandType,
    		reverseComplement=TRUE, genomeBuild="mm9",
    		uniqueHit = TRUE, assignMultihits = TRUE,
    		rerunWithDisambiguatedMultihits = FALSE,
    		multicore=multicore, silentMain=FALSE, verbose=TRUE)

    mainSeekOutputCTL <- mainSeek(bamFiles=grep(pattern="SRR039214",
        bamFiles, value=TRUE, invert=FALSE),
    		binSize=binSize, strandType=strandType,
    		reverseComplement=TRUE, genomeBuild="mm9",
    		uniqueHit = TRUE, assignMultihits = TRUE,
    		rerunWithDisambiguatedMultihits = FALSE,
    		multicore=multicore, silentMain=FALSE, verbose=TRUE)

    ################ Compute log score and test for significance WITH control ################
    ripGR.wicontrol <- logScoreWithControl(mainSeekOutputRIP$nbhGRList$chrX, mainSeekOutputCTL$nbhGRList$chrX)

    ripGR.wicontrol
    }

[Package RIPSeeker version 1.24.0 Index]