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Updated: Jul 15, 2026

Cell Type-specific Gene Expression Profiling in the Mouse Liver

Cell Type-specific Gene Expression Profiling in the Mouse Liver

Published on: September 17, 2019

Evolutionary-conserved gene expression response profiles across mammalian tissues.

Ji Chen¹, Thomas W Blackwell, Damian Fermin

¹Bioinformatics Program, University of Michigan, Ann Arbor, Michigan 48109, USA.

Omics : a Journal of Integrative Biology

|April 7, 2007

Summary

This study reveals conserved gene expression patterns across mammals, identifying 12 major evolutionarily conserved gene expression modes (CGEMs) that explain 84% of gene expression variation. These findings highlight conserved biological responses independent of experimental differences.

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Area of Science:

Genomics
Comparative Genomics
Bioinformatics

Background:

Gene expression involves complex, coordinated patterns across multiple genes.
Understanding evolutionary conservation of these patterns is crucial for biological insight.

Purpose of the Study:

To identify and characterize evolutionarily conserved gene expression patterns between humans and mice.
To develop a method for extracting conserved biological responses from gene expression data.

Main Methods:

Comparative analysis of human and mouse gene expression profiles across normal mammalian tissues.
Application of principal component analysis to identify gene expression modes.
Functional annotation using Gene Ontology, pathway analysis, and transcription factor binding site enrichment.

Main Results:

Identified 13 distinct gene expression modes in both human and mouse datasets.
Observed a striking 1-to-1 pairing for 12 out of 13 identified modes, defining conserved gene expression response modes (CGEMs).
CGEMs explained 84% of the total variation in the gene expression dataset, demonstrating robustness against experimental variability.

Conclusions:

Discovered an unbiased method to identify CGEMs, which are evolutionarily conserved across mammals.
Validated the conservation of major gene expression response modes, providing a robust framework for understanding biological regulation.