Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT.

ABSTRACT: Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contr

SUBMITTER: Du JH 

PROVIDER: S-EPMC9894175 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature