Description Usage Arguments Details Value Author(s) References See Also Examples
Filter out potentially false positive peaks from MosaicsPeak
class object, which is a peak calling result.
1 2 3 4 5 | filterPeak( object, ... )
## S4 method for signature 'MosaicsPeak'
filterPeak( object,
minSummitQuantile=0.01, minRead=10, FC=50, minSignal = 20, minLen = 200,
normC=NA, parallel=FALSE, nCore=8 )
|
object |
Object of class |
minSummitQuantile |
Parameter for the filtering step #1. Peaks are filtered out if signal strengths at summits < |
minRead |
Parameter for the filtering step #1. Peaks are filtered out if the number of reads in the peak region < |
FC |
Parameter for the filtering step #1. Peaks are filtered out if the improvement of ChIP over matched control < |
minSignal |
Parameter for the filtering step #2. Peaks are filtered out if signal strengths at summits <= |
minLen |
Parameter for the filtering step #2. Peaks are filtered out if their lengths <= |
normC |
Normalizing constant. If not provided,
|
parallel |
Utilize multiple CPUs for parallel computing
using |
nCore |
Number of CPUs when parallel computing is utilized. |
... |
Other parameters to be passed through to generic |
filterPeak
filters out potentially false positive peaks, based on signal strengths and peak lengths. While filterPeak
can be applied to a peak list object obtained using either functions mosaicsPeak
or mosaicsPeakHMM
, filterPeak
is developed and tested mainly for peak lists from MOSAiCS-HMM model (i.e., from function mosaicsPeakHMM
). Note that extractReads
should be run first because filterPeak
is used.
Construct MosaicsPeak
class object.
Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
Kuan, PF, D Chung, G Pan, JA Thomson, R Stewart, and S Keles (2011), "A Statistical Framework for the Analysis of ChIP-Seq Data", Journal of the American Statistical Association, Vol. 106, pp. 891-903.
Chung, D, Zhang Q, and Keles S (2014), "MOSAiCS-HMM: A model-based approach for detecting regions of histone modifications from ChIP-seq data", Datta S and Nettleton D (eds.), Statistical Analysis of Next Generation Sequencing Data, Springer.
mosaicsPeak
, mosaicsPeakHMM
,
extractReads
, findSummit
, adjustBoundary
,
MosaicsPeak
.
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 | ## Not run:
library(mosaicsExample)
constructBins( infile=system.file( file.path("extdata","wgEncodeBroadHistoneGm12878H3k4me3StdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
fileFormat="bam", outfileLoc="./",
byChr=FALSE, useChrfile=FALSE, chrfile=NULL, excludeChr=NULL,
PET=FALSE, fragLen=200, binSize=200, capping=0 )
constructBins( infile=system.file( file.path("extdata","wgEncodeBroadHistoneGm12878ControlStdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
fileFormat="bam", outfileLoc="./",
byChr=FALSE, useChrfile=FALSE, chrfile=NULL, excludeChr=NULL,
PET=FALSE, fragLen=200, binSize=200, capping=0 )
binHM <- readBins( type=c("chip","input"),
fileName=c( "./wgEncodeBroadHistoneGm12878H3k4me3StdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt",
"./wgEncodeBroadHistoneGm12878ControlStdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt" ) )
fitHM <- mosaicsFit( binHM, analysisType="IO", bgEst="rMOM" )
hmmHM <- mosaicsFitHMM( fitHM, signalModel = "2S",
init="mosaics", init.FDR = 0.05, parallel=TRUE, nCore=8 )
peakHM <- mosaicsPeakHMM( hmmHM, FDR = 0.05, decoding="posterior",
thres=10, parallel=TRUE, nCore=8 )
peakHM <- extractReads( peakHM,
chipFile=system.file( file.path("extdata","wgEncodeBroadHistoneGm12878H3k4me3StdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
chipFileFormat="bam", chipPET=FALSE, chipFragLen=200,
controlFile=system.file( file.path("extdata","wgEncodeBroadHistoneGm12878ControlStdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
controlFileFormat="bam", controlPET=FALSE, controlFragLen=200, parallel=TRUE, nCore=8 )
peakHM <- findSummit( peakHM, parallel=TRUE, nCore=8 )
peakHM <- adjustBoundary( peakHM, parallel=TRUE, nCore=8 )
peakHM <- filterPeak( peakHM, parallel=TRUE, nCore=8 )
## End(Not run)
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