|Description||Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms|
|Language||R (package), HTML (website)|
|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. https://doi.org/10.1016/j.isci.2018.10.028|
|Help and Support|
|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.
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 .
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:
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):
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.
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 .
This website is for educational and research purposes only.