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

Manqi Zhou·Hao Zhang·Zilong Bai·Dylan Mann-Krzisnik·Fei Wang·Yue Li
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Abstract

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.

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Deep LearningSingle-Cell AnalysisHumansComputational Biologybioinformaticscomputer sciencesgenomicssingle cellJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't

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Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, Yue Li. 2024-05-14. Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.. https://doi.org/10.1016/j.xpro.2024.103066

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