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Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Related Experiment Video

Updated: Aug 16, 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 Su¹, Zichun Xu¹, Xinning Shan¹

¹Department of Biostatistics, Yale University.

Biorxiv : the Preprint Server for Biology

|December 23, 2022

Summary

This summary is machine-generated.

We developed CS-CORE, a new statistical method to accurately infer gene co-expressions in specific cell types using single-cell RNA sequencing (scRNA-seq) data. This approach overcomes challenges like sequencing depth variations and measurement errors, improving biological insights into diseases.

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Last Updated: Aug 16, 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

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Area of Science:

Genomics
Computational Biology
Biostatistics

Background:

Gene co-expression analysis provides insights into biological processes and disease mechanisms.
Bulk RNA sequencing data confounds co-expression analysis due to mixed cell types.
Single-cell RNA sequencing (scRNA-seq) enables cell-type-specific co-expression inference but faces challenges with data variability and errors.

Conclusions:

CS-CORE is a superior method for inferring cell-type-specific gene co-expressions from scRNA-seq data.
The approach effectively handles data imperfections, leading to more reliable biological discoveries.
CS-CORE enhances our understanding of cell-type-specific gene regulation in disease contexts.