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

TitleSingle-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures.
Publication TypeJournal Article
Year of Publication2023
AuthorsZhou M, Zhang H, Bai Z, Mann-Krzisnik D, Wang F, Li Y
Date Published2023 Jan 31

The advent of single-cell multi-omics sequencing technology makes it possible for researchers 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 multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human peripheral blood mononuclear cells (PBMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Alternate JournalbioRxiv
PubMed ID36778483
PubMed Central IDPMC9915637
Grant ListRF1 AG072449 / AG / NIA NIH HHS / United States
Institute of Artificial Intelligence for Digital Health
Faculty Publication