dichotomizeCN: Dichotomizing copy number data based on segmented data (i.e....

In integIRTy: Integrating Multiple Modalities of High Throughput Assays Using Item Response Theory

Dichotomizing copy number data based on segmented data (i.e. log2ratio).

Description

A simple dichotomization procedure is implemented for CN data that only requires two cutoffs.

Usage

dichotomizeCN(CN, CNctr = NULL, tau1 = -0.3, tau2 = 0.3)

Arguments

CN	A matrix of gene wise copy number data for tumor samples. Rows are the genes; columns are the samples.
CNctr	A matrix of copy number data for normal samples. The program first guess if this is paired data by comparing if tumor and normal sample has equal sample size. If TRUE, then normal samples will be subtracted element by element to correct for germline CN change. Otherwise, no correction is performed. The program proceeds with only tumor data.
tau1	The lower bound for log2Ratio when converting to a code as 0.
tau2	The upper bound for log2Ratio when converting to a code as 0. Log2ratio between tau1 and tau2 is converted to 0 and 1 otherwise.

Value

Returns a binary matrix of the same dimension. Missing value would be propogated into the binary matrix.

Author(s)

Pan Tong ([e-mail]), Kevin R Coombes ([e-mail])

References

Tong P, Coombes KR. integIRTy: a method to identify altered genes in cancer accounting for multiple mechanisms of regulation using item response theory. Bioinformatics, 2012 Nov 15; 28(22):2861–9.

See Also

dichotomizeExpr, dichotomizeMethy

Examples

data(OV)
binDat <- dichotomizeCN(CN_T[1:20, ], CN_N[1:20, ])
binDat[15:20, 1:2]

integIRTy documentation built on May 3, 2022, 9:08 a.m.