DeMixT {DeMixT} | R Documentation

## 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.comp1, data.comp2 = NULL, niter = 10, nbin = 50,
    if.filter = TRUE, ngene.selected.for.pi = 250, mean.diff.in.CM = 0.25,
    tol = 10^(-5), output.more.info = FALSE,
    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.
`niter` | The maximum number of iterations used in the algorithm of iterated conditional modes (ICM, Ref[1]). 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.
`ngene.selected.for.pi` | The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixted tumor samples and the known component(s) are evaluated, and the most differentially expressed genes are selected. It is enabled when if.filter = TRUE. The default is 250.
`mean.diff.in.CM` | Threshold of expression difference in 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.
`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.
`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

`pi` | Matrix of estimated proportions for each known component. π1 corresponds to the proportion estimate for the first known component. π2 corresponds to the second known component.
`pi.iter` | Estimated proportions in each iteration. It is a number of iteration X Sy X 1 array in two-component setting, and a number_of_iteration X Sy X 2 array in three-component setting. This is enabled only when output.more.info = TRUE.
`ExprT` | 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` | 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` | 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` | Estimated μ of log2-normal distribution for both known (MuN1, MuN2) and unknown component (MuT).
`Sigma` | Estimated σ of log2-normal distribution for both known (SigmaN1, SigmaN2) and unknown component (SigmaT).
`gene.name` | The names of genes used in estimating the proportions. If no gene names are rpovided in the original data set, the genes will be automatically indexed. This is enabled only when output.more.info = TRUE.

### 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/DeMixT

### Examples

    # Example 1: simulated two-component data
    data(test.data1.y)
    data(test.data1.comp1)
    res <- DeMixT(data.Y = test.data1.y, data.comp1 = test.data1.comp1,
    if.filter = FALSE, output.more.info = TRUE)
    res$pi
    head(res$ExprT, 3)
    head(res$ExprN1, 3)
    head(res$Mu, 3)
    head(res$Sigma, 3)
    res$pi.iter
    res$gene.name

    # Example 2: simulated three-component data
    # It takes about 15 minutes to finish running
    # data(test.data2.y)
    # data(test.data2.comp1)
    # data(test.data2.comp2)
    # res <- DeMixT(data.Y = test.data2.y, data.comp1 = test.data2.comp1,
    #               data.comp2 = test.data2.comp2, if.filter = FALSE)

    # Example 3: three-component mixed cell line data applying

    # component merging strategy
    # It takes about 1.5 hours to finish running
    # data(test.data3.y)
    # data(test.data3.comp1)
    # data(test.data3.comp2)
    # res <- DeMixT(data.Y = test.data3.y, data.comp1 = test.data3.comp1,
    #               data.comp2 = test.data3.comp2, 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))

[Package DeMixT version 1.0.4 Index]