dichotomizeExpr {integIRTy}

Dichotomize the expression data given both tumor and normal samples.

Description

This function implements the z-like metric described in the paper.

Usage

dichotomizeExpr(expr, exprCtr, refUseMean = FALSE, BIthr = NULL,
                tau1 = -2.5, tau2 = 2.5, parallel = FALSE)

Arguments

expr

The expression matrix for tumor samples. Rows are genes and columns are samples.

exprCtr

Expression matrix of normal controls. Genes should exactly the same as the tumor sample. The sample size are not necessarily the same as tumor sample.

refUseMean

Logical indicating whether to use mean of normal sample as reference. Default is set to FALSE which means to use median as it is more robust.

BIthr

Threshold of bimodality index to flag bimodal genes. If not specified, it will be set according to the sample size of tumor samples. Specifically, if tumor sample size is over 100, BIthr=1.1. If sample size is between 50 and 100, BIthr=1.5. If sample size is below 50, BIthr=2.0.

tau1

Lower bound of z-like metric to be coded as 0.

tau2

Upper bound of z-like metric to be coded as 0. The z-like metric between tau1 and tau2 will be finally converted to 0 and otherwise.

parallel

Logical indicating whether to use parallel backend provided by foreach and related packages.

Details

The parallelism is written to speedup BI computation. If the number of genes is not large, i.e. below 4000, we recommend not to use parallel since this will only slow down the computation. In fact, except BI computation, all other operations are written with vector operation.

Value

A binary matrix of the same dimension of input expr. Missing values will be propogated into 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

dichotomizeCN, dichotomizeMethy, dichotomize

Examples

data(OV)
binDat <- dichotomizeExpr(Expr_T[1:200, ], Expr_N[1:200, ])
#binDat <- dichotomizeExpr(Expr_T[1:200, ], Expr_N[1:200, ], parallel=TRUE)
binDat[15:20, 1:2]