Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique

Abstract

Summary: The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study.For complete details on the use and execution of this protocol, please refer to Zhou et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Keywords

• bioinformatics
• single cell
• genomics
• computer sciences