Multi-modal quantification of pathway activity with MAYA

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities. Pathways can be activated through various signaling cascades depending on cell type. Here, the authors introduce MAYA, a computational method that can detect and score multiple modes of activation for each pathway, improving the granularity of pathway analysis for single-cell datasets.

Automated summary

This article introduces a computational method called MAYA, which can detect and score multiple modes of activation for each pathway in single-cell datasets. This method improves the granularity of pathway analysis and also has the ability to predict cell types and reveal common modes of pathway activation in tumor cells.