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

Publication date: Aug 28, 2023

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. Here, we propose an interpretable deep learning method called moETM to perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder and employs multiple linear decoders to learn the multi-omics signatures. moETM demonstrates superior performance compared with six state-of-the-art methods on seven publicly available datasets. By applying moETM to the scRNA + scATAC data, we identified sequence motifs corresponding to the transcription factors regulating immune gene signatures. Applying moETM to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omics biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Concepts Keywords
Biomarkers COVID-19
Challenging deep learning
Covid DL
Decoders embedding topipc model
Downstream ETM
Problem Solving
Research Personnel
RNA, Small Cytoplasmic
RNA, Small Cytoplasmic
single-cell multiomics
variational autoencoder


Type Source Name
disease IDO cell
disease MESH COVID-19
disease VO gene

Original Article

(Visited 1 times, 1 visits today)