DeMixT

Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

Latest release v1.20.1

DeMixT Overview

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 two or three cellular components (i.e. within heterogenous tissues such as cancers). DeMixT implements an iterated conditional mode algorithm and a gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application across large datasets of cancer studies, DeMixT showed high accuracy in inference of cell-type specific proportions[1-2]. Improved deconvolution is an important step towards linking tumor transcriptomic data with phenotypes and clinical outcomes.

A tutorial for running DeMixT can be found here.

demixt

Reference

[1] Ahn, J. et al. DeMix: Deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29, 1865–1871 (2013).

[2] Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018).

[3] Cao, S. et al. Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Nature Biotechnology Published online June 13 2022. doi 10.1038/s41587-022-01342-x.