Deep generative modeling of sample-level heterogeneity in single-cell genomics.

ABSTRACT: Single-cell genomic studies were recently conducted on hundred of samples exhibiting complex designs. These data have tremendous potential for discovering how sample- or tissue-level phenotypes relate to cellular and molecular composition. However, current analyses are often based on simplified representations of these data by averaging information across cells. We present multi-resolution variational inference (MrVI), a deep generative model designed to realize the potential of cohort studies at the single-cell level. MrVI tackles two fundamental, intertwined problems: stratifying samples into groups and evaluating the cellular and molecular differences between groups, without requiring predefined cell states. Leveraging its single-cell perspective, MrVI detects clinically relevant strati

SUBMITTER: Boyeau P 

PROVIDER: S-EPMC12615264 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature