Description Usage Arguments Details Value Note Author(s) References See Also Examples
Based on the posteriors derived from HMM by mainSeek
, find the significant RIP regions derived from merging the adjacent RIP bins. The significance test makes use of the log odd ratio of the posteriors for RIP over the background states.
1 2 3 
nbhGRRIP 

nbhGRCTL 
An optional argument as a 
padjMethod 
Method used to adjust multiple testing performed in 
logOddCutoff 
Threshold for the log odd ratio of posterior for the RIP over the background states (See 
pvalCutoff 
Threshold for the adjusted pvalue for the logOdd score. Only peaks with adjusted pvalue less than the 
pvalAdjCutoff 
Threshold for the adjusted pvalue for the logOdd score. Only peaks with adjusted pvalue less than the 
eFDRCutoff 
Threshold for the empirical false discovery rate (eFDR). Only peaks with eFDR less than the 
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. When control is available, the RIPScore is updated by the difference between the RIPScores between RIP and control. 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 pvalue computed using the R builtin function pnorm
.
The pvalue is adjusted by p.adjust
with BH method by default. The same procedure is applied optionally to the control library. Only when the control is available, is an empirical false discovery rate (eFDR) estimated based on the idea of "sample swap" inspired by MACS (a ChIPseq algorithm from Zhange el al. (2008). At each pvalue, RIPSeeker finds the number of significnat RIPregions over control (CTL) based on pvalRIP
and the number of significant control regions over RIP based on pvalCTL
. The eFDR is defined as the ratio of the number of "RIP" (false positive) regions identified from CTLRIP comparison over the number of RIP regions from the RIPCTL comparison. The maximum value for eFDR is 1 and minimum value for eFDR is max(pvalue, 0). The former takes care of the case where the numerator is bigger than the denominator, and the latter for zero numerator.
GRanges
object containing the merged bins with values slot saved for RIPScore (lodOdd), pvalue (pval), adjusted pvalue (pvalAdj) for RIP and optionally for control.
Internal function used by ripSeek
.
Yue Li
Yong Zhang, Tao Liu, Clifford A Meyer, J\'er\^ome Eeckhoute, David S Johnson, Bradley E Bernstein, Chad Nusbaum, Richard M Myers, Myles Brown, Wei Li, and X Shirley Liu. Modelbased analysis of ChIPSeq (MACS). Genome Biology, 9(9):R137, 2008.
logScoreWithControl, logScoreWithoutControl, empiricalFDR, computeLogOdd, scoreMergedBins, ripSeek
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  if(interactive()) {
# Retrieve system files
extdata.dir < system.file("extdata", package="RIPSeeker")
bamFiles < list.files(extdata.dir, ".bam$", recursive=TRUE, full.names=TRUE)
bamFiles < grep("PRC2", bamFiles, value=TRUE)
# Parameters setting
binSize < 1e5 # use a large fixed bin size for demo only
multicore < FALSE # use multicore
strandType < "" # set strand type to minus strand
################ run main function for HMM inference on all chromosomes ################
mainSeekOutputRIP < mainSeek(bamFiles=grep(pattern="SRR039214",
bamFiles, value=TRUE, invert=TRUE),
binSize=binSize, strandType=strandType,
reverseComplement=TRUE, genomeBuild="mm9",
uniqueHit = TRUE, assignMultihits = TRUE,
rerunWithDisambiguatedMultihits = FALSE,
multicore=multicore, silentMain=FALSE, verbose=TRUE)
mainSeekOutputCTL < mainSeek(bamFiles=grep(pattern="SRR039214",
bamFiles, value=TRUE, invert=FALSE),
binSize=binSize, strandType=strandType,
reverseComplement=TRUE, genomeBuild="mm9",
uniqueHit = TRUE, assignMultihits = TRUE,
rerunWithDisambiguatedMultihits = FALSE,
multicore=multicore, silentMain=FALSE, verbose=TRUE)
# with control
ripGR.wicontrol < seekRIP(mainSeekOutputRIP$nbhGRList$chrX, mainSeekOutputCTL$nbhGRList)
# without control
ripGR.wocontrol < seekRIP(mainSeekOutputRIP$nbhGRList$chrX)
}

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