Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.

ABSTRACT: Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent dataset

SUBMITTER: Marghi Y 

PROVIDER: S-EPMC10592946 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature