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 present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available.
This is the Github directory for older versions of DeMixT. Please visit github.com/wwylab/DeMixT for the lastest version.
A deconvolution framework for mixed transcriptomes from heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms
DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compart-ment-specific expression profiles for two-component and three-component deconvolution problem. Our method promises to provide deeper insight into cancer biomarkers and assist in the development of novel prognostic markers and therapeutic strategies.
The function DeMixT is designed to finish the whole pipeline of deconvolution for two or three components. DeMixT.S1 function is designed to estimate the proportions of all mixed samples for each mixing component. DeMixT.S2 function is designed to estimate the component-specific deconvolved expressions of individual mixed samples for a given set of genes.
DeMixT source files are compatible with windows, linux and mac os.
For users who have OpenMP on the computer, please use DeMixT_0.2 (DeMixT_0.1 is an archived version). To install this package, start R and enter:
devtools::install_github("wwylab/DeMixTallmaterials/DeMixT_0.2")
For users who do not have OpenMP on the computer, please use DeMixT_0.2.1 (DeMixT_0.1.1 is an archived version). To install this package, start R and enter:
devtools::install_github("wwylab/DeMixTallmaterials/DeMixT_0.2.1")
You can also download the installation files directly from the website:
DeMixT_0.2: http://bioinformatics.mdanderson.org/Software/DeMixT/DeMixT_0.2.tar.gz
DeMixT_0.2.1: http://bioinformatics.mdanderson.org/Software/DeMixT/DeMixT_0.2.1.tar.gz
For more information, please visit: http://bioinformatics.mdanderson.org/main/DeMixT
We provide a brief instruction for installing OpenMP, which is needed to enable the parallel computing for DeMixT. Please check the file "How_to_install_OpenMP.pdf".
The Genotype-Tissue Expression (GTEx) project provides a comprehensive public resource to study tissue-specific gene expression and regulation [1]. RNA sequencing data from 42 normal prostate samples, 67 normal thyroid samples and 20 normal lung samples without significant pathology in the corresponding tissue types are downloaded. For more information on how DeMixT uses unmatched reference profile data from the GTEx study, please refer to the vignette and our recent bioRxiv preprint [2].
This data set is used in our validation experiment for DeMixT. To generate this dataset in RNA-seq, we performed a mixing experiment, in which we mixed mRNAs from three cell lines: lung adenocarcinoma in humans (H1092), cancer-associated fibroblasts (CAFs) and tumor infiltrating lymphocytes (TIL), at different proportions to generate 32 samples, including 9 samples that correspond to three repeats of a pure cell line sample for three cell lines. The RNA amount of each tissue in the mixture samples was calculated on the basis of real RNA concentrations tested in the biologist’s lab.
Knitr documentation for the DeMixT paper (Wang et al.) can be downloaded from the website: http://bioinformatics.mdanderson.org/Software/DeMixT/online_methods.html.
[1] GTEx Consortium. "The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans." Science 348.6235 (2015): 648-660.
[2] Cao, Shaolong, et al. "Differing total mRNA expression shapes the molecular and clinical phenotype of cancer." bioRxiv (2020). https://www.biorxiv.org/content/10.1101/2020.09.30.306795v1.