AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).

Automated summary

AutoGeneS is a platform that automatically extracts discriminative genes and reveals cellular heterogeneity of bulk RNA samples without prior knowledge about marker genes. By simultaneously optimizing multiple criteria, AutoGeneS selects the most valuable genes for inferring cell-type proportions. Ground truth cell proportions analyzed by flow cytometry confirm the accuracy of AutoGeneS in identifying cell-type proportions.