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Related Experiment Video

Updated: Nov 30, 2025

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Published on: November 13, 2017

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Chunman Zuo¹, Luonan Chen^1,2,3

¹Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.

Briefings in Bioinformatics

|November 17, 2020

Summary

We developed a new computational model to integrate single-cell transcriptomic and chromatin accessibility data. This method effectively analyzes complex biological data, revealing cellular heterogeneity and regulatory mechanisms.

Keywords:

data integration deep joint-learning model multimodal variational autoencoder single-cell multiple omics data

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Area of Science:

Single-cell biology
Computational biology
Genomics

Background:

Simultaneous profiling of transcriptomic and chromatin accessibility data from individual cells provides high-resolution insights into cell states.
Existing computational methods struggle with integrating sparse and heterogeneous multimodal single-cell data effectively.

Purpose of the Study:

To develop a computationally effective method for integrating single-cell multimodal omics data.
To accurately represent multilayer cell profiles in a joint latent space.

Main Methods:

A single-cell multimodal variational autoencoder model was developed.
The model incorporates three joint-learning strategies and a probabilistic Gaussian Mixture Model.
The approach learns joint latent features from transcriptomic and chromatin accessibility data.

Main Results:

The model demonstrates superior performance in dissecting cellular heterogeneity within the joint latent space.
It shows enhanced capabilities in data denoising and imputation.
The method effectively constructs associations between multimodal omics data.

Conclusions:

The developed model offers a powerful tool for integrating sparse and heterogeneous single-cell multimodal data.
It facilitates a deeper understanding of cellular heterogeneity and transcriptional regulatory mechanisms.