CENTIPEDE-package: CENTIPEDE a probabilistic model for learning DNaseI...

In CENTIPEDE: CENTIPEDE learns a DNaseI footprint of a transcription factor and predicts its binding sites

Description

Centipede fits a bayesian hierarchical mixture model to learn TF-specific distribution of experimental data on a particular cell-type for a set of candidate binding sites described by a motif. More documentation is under preparation and will be make available soon at http://centipede.uchicago.edu

Details

Package:	CENTIPEDE
Type:	Package
Version:	1.2
Date:	2010-10-27
License:	GPL
URL:	http://centipede.uchicago.edu
LazyLoad:	yes

Author(s)

Roger Pique-Regi <[e-mail]> and Jacob F Degner <[e-mail]> Maintainer: Roger Pique-Regi <[e-mail]>

References

Roger Pique-Regi, Jacob F. Degner, Athma A. Pai, Daniel J. Gaffney, Yoav Gilad, Jonathan K. Pritchard. "Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data", Genome Research, Submitted Aug 2010

See Also

fitCentipede, plotProfile

Examples

## Not run: 
#GETS EXAMPLE DATA FOR NRSF
data(NRSFcuts, package='CENTIPEDE')
data(NRSF_Anno, package='CENTIPEDE')

#FITS THE CENTIPEDE MODEL
centFit <- fitCentipede(Xlist = list(as.matrix(NRSFcuts)), Y=cbind(rep(1, dim(NRSF_Anno)[1]), NRSF_Anno[,5], NRSF_Anno[,6]))

#PLOTS IMAGE OF CUTSITES RANKED BY CENTIPEDE POSTERIORS
imageCutSites(NRSFcuts[order(centFit$PostPr),][c(1:100, (dim(NRSFcuts)[1]-100):(dim(NRSFcuts)[1])),])

#PLOT ESTIMATED FOOTPRINT
plotProfile(centFit$LambdaParList[[1]],Mlen=21)

## End(Not run)

CENTIPEDE documentation built on May 2, 2019, 6:50 p.m.