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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 15, 2025

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

scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration.

Yunfan Li¹, Dan Zhang², Mouxing Yang¹

¹School of Computer Science, Sichuan University, Chengdu, Sichuan, China.

Nature Communications

|September 28, 2023

Summary

This summary is machine-generated.

Cell heterogeneity can improve single-cell multi-omics integration. scBridge exploits this by integrating cells heterogeneously, reducing omics differences and enhancing data analysis for improved biological insights.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell multi-omics data integration seeks to align different data types while preserving cell identity.
Cellular heterogeneity complicates distinguishing between omics and cell-type specific variations.
Existing methods often struggle to account for this inherent biological variability.

Purpose of the Study:

To develop a novel method for single-cell multi-omics data integration that leverages cell heterogeneity.
To improve the accuracy and robustness of integrating diverse omics datasets from single cells.
To address the challenge of distinguishing omics differences from cell-type differences in heterogeneous cell populations.

Main Methods:

Proposed scBridge, a heterogeneous multi-omics integration method.
scBridge iteratively identifies cells with minimal omics differences (reliable cells).
Integrates these reliable cells with other omics data (e.g., scRNA-seq) to bridge the omics gap.

Main Results:

Demonstrated the effectiveness of exploiting cell heterogeneity for data integration.
scBridge successfully reduced omics differences while maintaining cell type distinctions.
Outperformed six representative baseline methods across seven multi-omics datasets.

Conclusions:

Cell heterogeneity is a valuable feature, not just noise, for multi-omics integration.
scBridge offers a superior approach to single-cell multi-omics data integration by handling heterogeneity.
The method enhances the ability to analyze complex biological systems using multi-omics data.