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Cell Specific Gene Expression

Cell Specific Gene Expression

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Updated: Jun 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates

Yupu Xu¹, Yuzhou Wang², Shisong Ma³

¹MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China.

Cell Reports Methods

|July 6, 2024

Summary

This summary is machine-generated.

We developed SingleCellGGM, a novel algorithm for gene co-expression network analysis in single-cell transcriptomics. This method identifies gene expression programs (GEPs) for accurate cell-type annotation and cross-dataset label transfer.

Keywords:

CP: Systems biology cell-type label transfer gene co-expression network gene expression program graphical Gaussian model single-cell RNA-seq

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Published on: October 25, 2018

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Last Updated: Jun 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Published on: January 7, 2020

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level.
Gene co-expression network analysis is crucial for understanding gene function but is hindered by data sparsity (dropout values) in scRNA-seq.
Existing methods struggle to accurately capture functional gene relationships in sparse single-cell data.

Purpose of the Study:

To develop a robust algorithm for gene co-expression network analysis in single-cell transcriptomics.
To identify functional gene modules, termed gene expression programs (GEPs), from scRNA-seq data.
To enable accurate cell-type annotation and facilitate cross-dataset comparisons using GEPs.

Main Methods:

Development of a single-cell graphical Gaussian model (SingleCellGGM) algorithm.
Application of SingleCellGGM to mouse single-cell datasets to construct gene co-expression networks.
Identification and functional enrichment analysis of gene co-expression modules (GEPs).
Development of a GEP-based dimension reduction method.

Main Results:

SingleCellGGM successfully constructed gene co-expression networks from sparse single-cell data.
Identified GEPs demonstrated significant functional enrichment and were highly correlated with cell-type-specific functions.
GEPs enabled direct cell-type annotation without prior cell clustering.
GEPs showed conservation across datasets, facilitating universal cell-type label transfer.
GEP-based dimension reduction improved the interpretability of single-cell analysis results.

Conclusions:

SingleCellGGM provides a powerful tool for analyzing single-cell transcriptomes and overcoming dropout value challenges.
The identified GEPs represent conserved biological programs that can be used for robust cell-type annotation and cross-study comparisons.
This GEP-centric approach offers a novel perspective for uncovering shared biological insights across diverse single-cell datasets.