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

Overview

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.

publication date

  • May 14, 2024

Research

keywords

  • Deep Learning
  • Single-Cell Analysis

Identity

Digital Object Identifier (DOI)

  • 10.1016/j.xpro.2024.103066

PubMed ID

  • 38748882

Additional Document Info

volume

  • 5

issue

  • 2