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Related Experiment Video

Updated: Jul 19, 2025

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Cell-type-specific co-expression inference from single cell RNA-sequencing data.

Chang Su^1,2, Zichun Xu^1,3, Xinning Shan¹

¹Department of Biostatistics, Yale University, New Haven, CT, USA.

Nature Communications

|August 10, 2023

Summary

This summary is machine-generated.

This study introduces CS-CORE, a new statistical method for analyzing gene co-expression in single-cell RNA sequencing (scRNA-seq) data. CS-CORE accurately estimates co-expressions, overcoming challenges like sequencing depth variations and measurement errors, leading to more reliable biological insights.

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Last Updated: Jul 19, 2025

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Published on: October 25, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables cell-type-specific gene co-expression analysis.
Existing methods struggle with high sequencing depth variations and measurement errors inherent in scRNA-seq data.
Accurate co-expression inference is crucial for understanding cell-type-specific biological functions.

Purpose of the Study:

To develop a robust statistical approach for estimating and testing cell-type-specific co-expressions in scRNA-seq data.
To address the challenges of sequencing depth variations and measurement errors.
To improve the accuracy and reliability of co-expression analysis in complex biological samples.

Main Methods:

Proposed a novel statistical method, CS-CORE.
Explicitly modeled sequencing depth variations and measurement errors in scRNA-seq data.
Systematically evaluated CS-CORE against existing methods using simulated and real-world datasets.

Main Results:

CS-CORE provided accurate co-expression estimates and clustering analysis, unlike existing methods which showed inflated false positives and biased results.
CS-CORE identified reproducible and biologically relevant cell-type-specific co-expressions and differential co-expressions.
The method was validated on scRNA-seq data from Alzheimer's disease and COVID-19 patient samples.

Conclusions:

CS-CORE offers a significant advancement in analyzing scRNA-seq data for gene co-expression.
The method enhances the reliability and biological relevance of findings from scRNA-seq studies.
CS-CORE is a valuable tool for investigating cell-type-specific functions in health and disease.