Department of Bioinformatics and Computational Biology

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DescriptionCell 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
LanguageR (package), HTML (website)
Current version1.0.1
LicenseArtistic (package)
Last updated2019/05/30
Citation Wang, Z., Cao, S., Morris, J.S., Ahn, J., Liu, R., Tyekucheva, S., Gao, F., Li, B., Lu, W., Tang, X. and Wistuba, I.I., (2018). Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 9, pp.451-460.
Help and Support
Contact Zeya Wang 
Shaolong Cao 
Wenyi Wang 
Discussion Issues on GitHub 


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.


Latest update.

We recommend using the Biocondcutor R package DeMixT 1.0.1 for your analysis. R.3.6.0 is required.

For users who have OpenMP installed.

You may also download DeMixT_0.2.tar.gz . The source file is compatible with Windows, Linux and macOS.

For users who do not have OpenMP installed.

You may also download DeMixT_0.2.1.tar.gz . The source file is compatible with Windows, Linux and macOS.

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

R Package Installation

Github link for DeMixT source code: .

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:


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


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

res <- DeMixT(data.Y = test.data1$y, data.comp1 = test.data1$comp1, if.filter = FALSE) 
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 COAD three-component proportions: COAD_deconvolved_proportions.csv .

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


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

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


The deconvolved PRAD three-component proportions: PRAD_deconvolved_proportions.csv .

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


For questions or support related to the use of either the DeMixT R package or web app, please visit the project on GitHub .
For other inquiries, please contact Wenyi Wang .


This website is for educational and research purposes only.