The cell composition and genetics of the tumour microenvironment have been shown to be key for the progression of a diverse range of cancers1,2, as well as having prognostic predictive power (e.g., for colorectal cancer3,4). However, the analysis of tissue cell composition and the genetics of single cell types usually requires flow cytometry technologies and tissue processing that ultimately affect the cell molecular content (e.g., RNA) and which are poorly scalable to large cohorts.
Here, I present ARMET (algorithm for resolving microenvironment transcriptomes) a robust, innovative computational tool for mapping whole cancer tissue transcriptional changes to single cell-types components (e.g., epithelial, fibroblasts and lymphocytes); in doing so the tissue composition is inferred for each tissue sample using the transcriptional data, with greater accuracy than any other publicly available tool. This tool is particularly suitable for large RNA-seq cohorts such as TCGA and PCAWG, and unlocks the high-throughput exploration of the cancer microenvironment at the molecular level, improving the resolution and interpretability of complex transcriptome data sets.