A deep generative framework with embedded vector arithmetic and classifier for sample generation, label transfer, and clustering of single-cell data.

ABSTRACT: Multiple-source single-cell datasets have accumulated quickly and need computational methods to integrate and decompose into meaningful components. Here, we present inClust (integrated clustering), a flexible deep generative framework that enables embedding auxiliary information, latent space vector arithmetic, and clustering. All functional parts are relatively modular, independent in implementation but interrelated at runtime, resulting in an all-in general framework that could work in supervised, semi-supervised, or unsupervised mode. We show that inClust is superior to most data integration methods in benchmark datasets. Then, we demonstrate the capability of inClust in the tasks of conditional out-of-distribution generation in supervised mode, label transfer in semi-supervised mode, a

SUBMITTER: Wang L 

PROVIDER: S-EPMC10475846 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature