DeMixT: Deconvolution of heterogeneous tumor samples with two or...

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

Deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

Description

DeMixT is a software that performs deconvolution on transcriptome data from a mixture of two or three components.

Usage

DeMixT(
  data.Y,
  data.N1,
  data.N2 = NULL,
  niter = 10,
  nbin = 50,
  if.filter = TRUE,
  filter.sd = 0.5,
  ngene.selected.for.pi = NA,
  mean.diff.in.CM = 0.25,
  nspikein = NULL,
  gene.selection.method = "GS",
  ngene.Profile.selected = NA,
  tol = 10^(-5),
  output.more.info = FALSE,
  pi01 = NULL,
  pi02 = NULL,
  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.
niter	The maximum number of iterations used in the algorithm of iterated conditional modes. A larger value better guarantees the convergence in estimation but increases the running time. The default is 10.
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.
if.filter	The logical flag indicating whether a predetermined filter rule is used to select genes for proportion estimation. The default is TRUE.
filter.sd	The cut-off for the standard deviation of lognormal distribution. Genes whose log transferred standard deviation smaller than the cut-off will be selected into the model. The default is 0.5.
ngene.selected.for.pi	The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixed tumor samples and the known component(s) are evaluated, and the most differential expressed genes are selected, which is called DE. It is enabled when if.filter = TRUE. The default is min(1500, 0.3*G), where G is the number of genes. Users can also try using more genes, ranging from 0.3*G to 0.5*G, and evaluate the outcome.
mean.diff.in.CM	Threshold of expression difference for selecting genes in the component merging strategy. We merge three-component to two-component by selecting genes with similar expressions for the two known components. Genes with the mean differences less than the threshold will be selected for component merging. It is used in the three-component setting, and is enabled when if.filter = TRUE. The default is 0.25.
nspikein	The number of spikes in normal reference used for proportion estimation. The default value is min(200, 0.3*My), where My the number of mixed samples. If it is set to 0, proportion estimation is performed without any spike in normal reference.
gene.selection.method	The method of gene selection used for proportion estimation. The default method is 'GS', which applies a profile likelihood based method for gene selection. If it is set to 'DE', the most differential expressed genes are selected.
ngene.Profile.selected	The number of genes used for proportion estimation ranked by profile likelihood. The default is min(1500,0.1*G), where G is the number of genes. This is enabled only when gene.selection.method is set to 'GS'.
tol	The convergence criterion. The default is 10^(-5).
output.more.info	The logical flag indicating whether to show the estimated proportions in each iteration in the output.
pi01	Initialized proportion for first kown component. The default is Null and pi01 will be generated randomly from uniform distribution.
pi02	Initialized proportion for second kown component. pi02 is needed only for running a three-component model. The default is Null and pi02 will be generated randomly from uniform distribution.
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

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, and each column corresponds to one sample.
pi.iter	Estimated proportions in each iteration. It is a niter* My*p array, where p is the number of components. This is enabled only when output.more.info = TRUE.
ExprT	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.
ExprN1	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.
ExprN2	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.
Mu	A matrix of estimated Mu of log2-normal distribution for both known (MuN1, MuN2) and unknown component (MuT). Each row corresponds to one gene.
Sigma	Estimated Sigma of log2-normal distribution for both known (SigmaN1, SigmaN2) and unknown component (SigmaT). Each row corresponds to one gene.
gene.name	The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed.

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: simulated two-component data by using GS(gene selection method)
  data(test.data.2comp)
# res <- DeMixT(data.Y = test.data.2comp$data.Y, 
#               data.N1 = test.data.2comp$data.N1, 
#               data.N2 = NULL, nspikein = 50, 
#               gene.selection.method = 'GS',
#               niter = 10, nbin = 50, if.filter = TRUE, 
#               ngene.selected.for.pi = 150,
#               mean.diff.in.CM = 0.25, tol = 10^(-5))
# res$pi
# head(res$ExprT, 3)
# head(res$ExprN1, 3)
# head(res$Mu, 3)
# head(res$Sigma, 3)
# 
# Example 2: simulated two-component data by using DE(gene selection method)
# data(test.data.2comp)
# res <- DeMixT(data.Y = test.data.2comp$data.Y,
#               data.N1 = test.data.2comp$data.N1, 
#               data.N2 = NULL, nspikein = 50, g
#               ene.selection.method = 'DE',
#               niter = 10, nbin = 50, if.filter = TRUE, 
#               ngene.selected.for.pi = 150,
#               mean.diff.in.CM = 0.25, tol = 10^(-5))
#
# Example 3: three-component mixed cell line data applying 
# component merging strategy
# data(test.data.3comp)
# res <- DeMixT(data.Y = test.data.3comp$data.Y, 
#               data.N1 = test.data.3comp$data.N1,
#               data.N2 = test.data.3comp$data.N2, 
#               if.filter = TRUE)
#
# Example: convert a matrix into the SummarizedExperiment format
# library(SummarizedExperiment)
# example <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE)
# example.se <- SummarizedExperiment(assays = list(counts = example))

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