cptModel: Computes within and between gene feature profile statistics...

Description Usage Arguments Details Value Author(s) Examples

View source: R/cptModel.R

Description

Computes the percentiles on the estimated profile statistics within a gene and across genes for one or more combination of feature or data types (expression, methylation, copy-number variation, or variant change)

Usage

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cptModel(psm, genelist, cpt.data, cpt.method, cpt.max)

Arguments

psm

: A data matrix of estimated gene profile statistics for each feature

genelist

: A vector of gene names or gene symbols corrosponding to the profile statistics

cpt.data

: Identify changepoints in the data using variance (cpt.var), mean (cpt.mean) or both (meanvar). Default is cpt.var.

cpt.method

: Choice of single or multiple changepoint model. Default is "BinSeg".

cpt.max

: The maximum number of changepoints to search for using "BinSeg" method. Default is 60. This number is dependent on the number of input data points

Details

This function estimates within and between feature profile statistics by gens in addition to the summed percentiles and successive differences

Value

Estimated change points in the input data set

Author(s)

Bhakti Dwivedi & Jeanne Kowalski

Examples

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id <- 1000 ## number of probes
s <- 3 ## number of sample groups
dm <- matrix(runif(id*s,0,200), nrow=id, ncol=s, dimnames=list(paste("gene", 1:id, sep="") , paste("fs", 1:s, sep="")))
genelist <- rownames(dm)
cptModel(dm, genelist, cpt.data="var", cpt.method="BinSeg", cpt.max=60)

GISPA documentation built on Nov. 1, 2018, 2:03 a.m.