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PeakSegDP (version 2017.08.15)

Dynamic Programming Algorithm for Peak Detection in ChIP-Seq Data

Description

A quadratic time dynamic programming algorithm can be used to compute an approximate solution to the problem of finding the most likely changepoints with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, etc. For more info read "PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data" by TD Hocking et al, proceedings of ICML2015.

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Version

Install

install.packages('PeakSegDP')

Monthly Downloads

697

Version

2017.08.15

License

GPL-3

Maintainer

Toby Hocking

Last Published

August 15th, 2017

Functions in PeakSegDP (2017.08.15)

GeomTallRect

GeomTallRect
H3K36me3.AM.immune.19

Several ChIP-seq profiles, some of which have few data points
chr11ChIPseq

ChIP-seq aligned read coverage for 4 samples on a subset of chr11
chr11first

Counts of first base of aligned reads
calc.loss.from.lp.list

calc loss from lp list
calc.loss.list

calc loss list
H3K36me3.TDH.other.chunk3.cluster4

8 profiles of H3K36me3 data
H3K4me3.TDH.immune.chunk12.cluster4

Histone ChIP-seq data, 26 samples, chr1 subset
derivs

derivs
getPath

getPath
cDPA

cDPA
calc.grad.list

calc grad list
phi.list

phi list
regression.funs

regression funs
PeakSegDP

PeakSegDP
PoissonLoss

PoissonLoss