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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 microarray-based...

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Updated: May 26, 2026

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

Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.

Tomoya Furutani^1,2, Hongkai Ji¹

¹Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21212, USA.

Biorxiv : the Preprint Server for Biology

|May 25, 2026

Summary

This summary is machine-generated.

We introduce the Cross-modality Link Confidence (CLIC) score to quantify gene expression and chromatin accessibility concordance. This score improves the integration of single-cell RNA-seq and ATAC-seq data, enhancing biological insights.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell and spatial omics technologies generate vast datasets, but most measure only one molecular layer.
Integrating separately profiled single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data is challenging due to variable correlations between gene expression and chromatin accessibility.
Low-confidence gene-peak associations can reduce the accuracy of integrative computational methods.

Purpose of the Study:

To develop a quantitative measure for the concordance between gene expression and chromatin accessibility in single-cell data.
To introduce a novel feature selection strategy that leverages this concordance measure to improve cross-modal data integration.
To enhance the robustness and biological interpretability of integrated single-cell omics datasets.

Main Methods:

Developed the Cross-modality Link Confidence (CLIC) score using diverse single-cell multiome datasets from the ENCODE project.
Quantified the empirical concordance between gene expression and nearby chromatin accessibility.
Implemented a hybrid feature selection strategy combining highly variable genes with high-CLIC genes.

Main Results:

CLIC scores provide reliable prior confidence estimates for gene-peak associations across modalities.
The hybrid feature selection strategy significantly improves the integration of gene expression and chromatin accessibility data.
The proposed approach demonstrated consistent performance across various public single-cell and spatial datasets and integration frameworks.

Conclusions:

The CLIC score offers a robust method for assessing gene-regulatory element relationships in single-cell genomics.
Integrating gene expression and chromatin accessibility data using CLIC-informed feature selection enhances analytical power and biological discovery.
This work advances the computational toolkit for multimodal single-cell data analysis.