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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial

Yang Xu¹, Edmon Begoli^2,3, Rachel Patton McCord⁴

¹UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA.

NPJ Systems Biology and Applications

|September 11, 2022

Summary

We developed sciCAN, a novel adversarial method for unsupervised integration of single-cell chromatin accessibility and gene expression data. sciCAN shows consistent performance and better cross-modal transfer, preserving biological relationships and identifying cellular responses to perturbations.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell technologies generate high-dimensional data from diverse sources.
Integrating single-cell data across different modalities (e.g., chromatin accessibility and gene expression) presents a significant computational challenge.

Purpose of the Study:

To present sciCAN, an adversarial approach for unsupervised integration of single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) data.
To demonstrate sciCAN's effectiveness in handling multi-modal single-cell data integration.

Main Methods:

An adversarial integration framework named sciCAN was developed.
Benchmarking involved comparing sciCAN against 5 existing methods using 5 scATAC-seq/scRNA-seq datasets.
Application to 10X Multiome data and CRISPR-perturbed single-cell data was performed.

Main Results:

sciCAN demonstrated consistent performance across multiple datasets.
The method achieved a better balance of mutual information transfer between modalities compared to existing methods.
The integrated representation from sciCAN preserved biological relationships within the hematopoietic hierarchy.
sciCAN successfully identified cells with related responses to different perturbations in multi-modal data.

Conclusions:

sciCAN provides a robust and effective solution for unsupervised integration of multi-modal single-cell data.
The approach enhances the understanding of cellular systems by integrating diverse data types.
sciCAN is a valuable tool for analyzing complex biological systems, including responses to genetic perturbations.