cDPA: cDPA

In PeakSegDP: Dynamic Programming Algorithm for Peak Detection in ChIP-Seq Data

Description

A constrained dynamic programming algorithm (cDPA) can be used to compute the best segmentation 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, ...

Usage

cDPA(count, weight = rep(1, length(count)), maxSegments)

Arguments

count	Integer vector of count data to segment.
weight	Data weights (normally this is the number of base pairs).
maxSegments	Maximum number of segments to consider.

Author(s)

Toby Dylan Hocking, Guillem Rigaill

Examples

fit <- cDPA(c(0, 10, 11, 1), maxSegments=3)
stopifnot(fit$ends[3,4] == 3)
stopifnot(fit$ends[2,3] == 1)

Example output

PeakSegDP documentation built on May 1, 2019, 7:55 p.m.