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.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)

[Package DeMixT version 1.4.0 Index]