simulate_2comp: Function to simulate two-component test data

In DeMixT: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

Description

Function to simulate two-component test data for DeMixT.

Usage

simulate_2comp(G = 500, My = 100, M1 = 100, output.more.info = FALSE)

Arguments

G	Number of genes for simulation.
My	Number of mixture tumor samples for simulation.
M1	Number of normal reference for simulation.
output.more.info	The logical flag indicating wheter to show True.data.T and True.data.N1 in the output. The default is FALSE.

Value

pi	A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings. Each column corresponds to one sample.
Mu	Simulated Mu of log2-normal distribution for both known (MuN1) and unknown component (MuT).
Sigma	Simulated Sigma of log2-normal distribution for both known (SigmaN1) and unknown component (SigmaT).
data.Y	A SummarizedExperiment object of expression data from mixed tumor samples. It is a G by My matrix where G is the number of genes and My is the number of mixed samples. Samples with the same tissue type should be placed together in columns.
data.N1	A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a G by M1 matrix where G is the number of genes and M1 is the number of samples for component 1.
True.data.T	A SummarizedExperiment object of simulated tumor expression data. It is a G by My matrix, where G is the number of genes and My is the number of mixed samples. This is enabled only when output.more.info = TRUE.
True.data.N1	A SummarizedExperiment object of simulated true expression data for reference component 1 (e.g., normal). It is a G by M1 matrix where G is the number of genes and M1 is the number of samples for component 1. This is enabled only when output.more.info = TRUE.

Examples

test.data = simulate_2comp(G = 500, My = 100, M1 = 100)
test.data$pi
test.data$Mu
test.data$Sigma

DeMixT documentation built on Nov. 8, 2020, 6:41 p.m.