DeMixT_S2: Deconvolves expressions of each individual sample for unknown...

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

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.N1,
  data.N2 = 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 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.
data.N2	A SummarizedExperiment object of expression data from additional reference samples. It is a G by M2 matrix where G is the number of genes and M2 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 unknown reference component, and in three-component analysis, it gives the proportions for the two known components.
nbin	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 available 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	A 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	A 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	A 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	A matrix of estimated Mu of log2-normal distribution for both known (MuN1, MuN2) and unknown component (MuT). Each row corresponds to one gene.
decovSigma	Estimated Sigma of log2-normal distribution for both known (SigmaN1, SigmaN2) and unknown component (SigmaT). Each row corresponds to one gene.

Author(s)

Zeya Wang, Wenyi Wang

References

Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451-460.

See Also

http://bioinformatics.mdanderson.org/main/DeMixT

Examples

# Example 1: two-component deconvolution given proportions 
  data(test.data.2comp)
  givenpi <- c(t(as.matrix(test.data.2comp$pi[-2,])))
  res.S2 <- DeMixT_S2(data.Y = test.data.2comp$data.Y, 
                      data.N1 = test.data.2comp$data.N1,
                      data.N2 = NULL, 
                      givenpi = givenpi, 
                      nbin = 50)
#                  
# Example 2: three-component deconvolution given proportions 
# data(test.data.3comp)
# givenpi = c(t(test.data.3comp$pi[-3,])) 
# res <- DeMixT_S2(data.Y = test.data.3comp$data.Y, 
#                  data.N1 = test.data.3comp$data.N1,
#                  data.N2 = test.data.3comp$data.N2, 
#                  givenpi = givenpi, 
#                  nbin = 50)

wwylab/DeMixT documentation built on July 17, 2024, 9:14 p.m.