DeMixT_S2 {DeMixT}R Documentation

Deconvolves expressions of each individual sample for unknown component

Description

This function is designed to estimate the deconvolved expressions of individual mixed tumor samples for unknown component for each gene.

Usage

DeMixT_S2(data.Y, data.comp1, data.comp2 = NULL, givenpi, nbin = 50, 
nthread = parallel::detectCores() - 1)

Arguments

data.Y

A SummarizedExperiment object of expression data from mixed tumor samples. It is a G by Sy matrix where G is the number of genes and Sy is the number of mixed samples. Samples with the same tissue type should be placed together in columns.

data.comp1

A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a G by S1 matrix where G is the number of genes and S1 is the number of samples for component 1.

data.comp2

A SummarizedExperiment object of expression data from additional reference samples. It is a G by S2 matrix where G is the number of genes and S2 is the number of samples for component 2. Component 2 is needed only for running a three-component model.

givenpi

A vector of proportions for all mixed tumor samples. In two-component analysis, it gives the proportions of the known reference component, and in three-component analysis, it gives the proportions for the two known components.

nbin

The number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a three-component deconvolution problem. The default is 50.

nthread

The number of threads used for deconvolution when OpenMP is availble in the system. The default is the number of whole threads minus one. In our no-OpenMP version, it is set to 1.

Value

decovExprT

Matrix of deconvolved expression profiles corresponding to T-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.

decovExprN1

Matrix of deconvolved expression profiles corresponding to N1-component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample.

decovExprN2

Matrix of deconvolved expression profiles corresponding to N2-component in mixed samples for a given subset of genes in a three-component setting. Each row corresponds to one gene and each column corresponds to one sample.

decovMu

Estimated μ of log2-normal distribution for both known (MuN1, MuN2) and unknown component (MuT).

decovSigma

Estimated σ of log2-normal distribution for both known (SigmaN1, SigmaN2) and unknown component (SigmaT).

Author(s)

Zeya Wang, Wenyi Wang

References

J. Besag. "On the statistical analysis of dirty pictures". In: Journal of the Royal Statistical Society. Series B (Methodological) (1986), pp. 259-302.

See Also

http://bioinformatics.mdanderson.org/main/DeMix:Overview

Examples

# Example 1: two-component deconvolution given proportions 
data(test.data1.truth)
data(test.data1.y)
data(test.data1.comp1)
givenpi <- c(t(as.matrix(test.data1.truth[-2,])))
res <- DeMixT_S2(data.Y = test.data1.y, 
data.comp1 = test.data1.comp1, givenpi = givenpi)

# Example 2: three-component deconvolution given proportions 
# This example takes 10 minutes to finish running
#  data(test.data2.truth)
#  data(test.data2.y)
#  data(test.data2.comp1)
#  data(test.data2.comp2)
#  givenpi <- c(t(test.data2.truth[-3,]))
#  res <- DeMixT_S2(data.Y = test.data2.y, data.comp1 = test.data2.comp1, 
#                   data.comp2 = test.data2.comp2, givenpi = givenpi)

[Package DeMixT version 1.0.4 Index]