Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures.

ABSTRACT: The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multipl

SUBMITTER: Zhou M 

PROVIDER: S-EPMC9915637 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature