### Department of Bioinformatics and Computational Biology

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DeMixT

 hidden row for table layout Overview Description Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Development Information GitHub wwylab/DeMixTallmaterials Language R (package), DHTML (website) Current version 0.2 & 0.2.1 License Artistic (package) Status Active Last updated 2019/02/28 References Citation Help and Support Contact Zeya Wang  Shaolong Cao  Wenyi Wang Discussion Issues on GitHub

## DeMixT

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We develop a three-component deconvolution model, DeMixT, for expression data from a mixture of cancerous tissues, infiltrating immune cells and tumor microenvironment. DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

Knitr documentation for all analyses performed in the DeMixT manuscript (Wang et al.) can be found here.

For users who have OpenMP installed.

For users who do not have OpenMP installed.

For the details about how to use DeMixT, please check the Manual .

### R Package Installation

Github link for DeMixT source code: https://github.com/wwylab/DeMixTallmaterials .

Our package is able to do parallel computing through OpenMP. For instruction on how to install OpenMP, please check the Instruction .

To install DeMixT package from Github for users who have OpenMP:

devtools::install_github("wwylab/DeMixTallmaterials/DeMixT_0.2")

To install DeMixT package from Github for users who do not have OpenMP:

devtools::install_github("wwylab/DeMixTallmaterials/DeMixT_0.2.1")

R script to run an example of DeMixT (version 0.2 or version 0.2.1):

data(test.data1)
res <- DeMixT(data.Y = test.data1$y, data.comp1 = test.data1$comp1, if.filter = FALSE)
res$pi head(res$decovExprT, 3)
head(res$decovExprN1, 3) head(res$decovMu, 3)
head(res$decovSigma, 3)  Results from DeMixT DeMixT returns a list containing the deconvolved results.The res\$pi is a matrix giving the estimated proportions for each known component, so it is one-dimensional for a two-component deconvolution and two-dimensional for a three-component deconvolution. Proportions of the unknown component can be calculated by 1-colSums(res\$pi). res\$decovExprT, res\$decovExprN1 (res\$decovExprN2) give matrices of deconvolved expression profiles corresponding to unknown T-component and known N1-component (N2-component), respectively. The res\$decovMu and res\$decovSigma are two matrices of estimated $\mu$ and $\sigma$ for all the components.

### Deconvolved Proportions

The deconvolved proportions and component-specific expression matrices (for each sample and each gene) for COAD, HNSC and PRAD are available.

The deconvolved component-specific expression matrices: COAD_deconvolved_expression.RData .

HNSC

The deconvolved HNSC three-component proportions: HNSC_deconvolved_proportions.csv .

The deconvolved component-specific expression matrices: HNSC_deconvolved_expression.RData .