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Updated: Jan 25, 2026
07:53
Single Cell Fate Mapping in Zebrafish
Published on: October 5, 2011
13.9K
Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions
Thalia E Chan1, Michael P H Stumpf1, Ann C Babtie2
1Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, UK.
Methods in Molecular Biology (Clifton, N.J.)
|May 8, 2019
Summary
This study introduces a network inference algorithm (PIDC) to analyze single-cell gene expression data. It helps uncover gene regulatory networks driving cell differentiation from complex expression patterns.
Area of Science:
- Computational Biology
- Genomics
- Systems Biology
Background:
- Single-cell experimental techniques enable large-scale gene expression quantification.
- These data reveal dynamic transcriptional changes during cell development and differentiation.
Purpose of the Study:
- To describe the use of the PIDC algorithm and NetworkInference.jl software.
- To infer functional gene interactions from single-cell gene expression patterns.
Main Methods:
- Utilizing large sample sizes and variability in single-cell data.
- Employing multivariate information measures to detect statistical dependencies between genes.
- Inferring gene regulatory networks using the PIDC algorithm.
Main Results:
- Identification of putative co-regulatory relationships between genes.
- Detection of complex statistical relationships indicative of gene interactions.
- Generation of gene regulatory network models.
Conclusions:
- The PIDC algorithm effectively infers gene regulatory networks from single-cell data.
- Network models provide insights into the mechanisms of cell differentiation.
- Guidelines are provided for integrating PIDC analysis with complementary methods.
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