Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.

MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. 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 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. 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. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease.

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context.

Maya hard surface modeling