mixModelParams: Generate the parameters for two 2-component Gaussian mixture...

In oncomix: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data

Description

This function allows you to generate the parameters for two 2-component Gaussian mixture model with equal variances from 2 matrices of data with a priori labels (eg tumor vs normal.) This application was originally intended for matrices of gene expression data treated with 2 conditions.

Usage

mixModelParams(exprNml, exprTum)

Arguments

exprNml	A dataframe (S3 or S4), matrix, or SummarizedExperiment object containing normal data with patients as columns and genes as rows.
exprTum	A dataframe (S3 or S4), matrix, or SummarizedExperiment object containing tumor data with patients as columns and genes as rows.

Value

Returns a dataframe, each element of which contains the 12 mixture model parameters for each gene in an n x 12 matrix, where n is the number of genes.

Examples

exprNml <- as.data.frame(matrix(data=rgamma(n=150, shape=2, rate=2),
nrow=10, ncol=15))
colnames(exprNml) <- paste0("patientN", seq_len(ncol(exprNml)))
rownames(exprNml) <- paste0("gene", seq_len(nrow(exprNml)))

exprTum <- as.data.frame(matrix(data=rgamma(n=150, shape=4, rate=3),
nrow=10, ncol=15))
colnames(exprTum) <- paste0("patientT", seq_len(ncol(exprTum)))
rownames(exprTum) <- paste0("gene", seq_len(nrow(exprTum)))

mmParams <- mixModelParams(exprNml, exprTum)

oncomix documentation built on Nov. 8, 2020, 5:39 p.m.