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Cell Specific Gene Expression

Cell Specific Gene Expression

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...

Cell Specific Gene Expression

Cell Specific Gene Expression

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Related Experiment Video

Updated: Feb 22, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.

Thalia E Chan¹, Michael P H Stumpf², Ann C Babtie¹

¹Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.

|September 29, 2017

Summary

This summary is machine-generated.

This study introduces PIDC, a new algorithm using partial information decomposition (PID) to uncover gene regulatory networks from single-cell gene expression data. PIDC effectively identifies complex gene relationships, outperforming older methods.

Keywords:

gene regulation mutual information network reconstruction single-cell PCR single-cell RNA-seq

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Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Single-cell gene expression experiments offer insights into cellular heterogeneity but pose data processing challenges.
Cell-to-cell variability in these datasets contains statistical relationships exploitable by information theory.
Understanding gene interactions is crucial for deciphering cellular mechanisms.

Purpose of the Study:

To develop and evaluate a novel algorithm, PIDC, for inferring gene regulatory relationships from single-cell transcriptomic data.
To leverage multivariate information theory and partial information decomposition (PID) for enhanced network inference.
To provide tools and tutorials for applying PIDC to biological data.

Main Methods:

Development of PIDC, a fast and efficient algorithm utilizing partial information decomposition (PID).
Multivariate information theory applied to analyze statistical dependencies among gene triplets in single-cell data.
Performance evaluation using simulated data and comparison with pairwise mutual information methods.
Inference of gene regulatory networks from three experimental single-cell datasets.

Main Results:

PIDC effectively captures higher-order information, outperforming pairwise methods in identifying true relationships in simulated data.
The algorithm successfully inferred gene regulatory networks from experimental single-cell datasets.
Analysis highlighted the impact of network context, analytical choices, and variability sources on network inference outcomes.

Conclusions:

PIDC offers a powerful approach for identifying putative functional relationships and generating mechanistic hypotheses from single-cell transcriptomic data.
The algorithm facilitates a deeper understanding of gene regulatory networks in complex biological systems.
Open-source software and tutorials are available to support the adoption of PIDC in the research community.