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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...

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Updated: Jun 27, 2026

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

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Published on: October 25, 2018

scHiGex: predicting single-cell gene expression based on single-cell Hi-C data.

Bishal Shrestha¹, Andrew Jordan Siciliano¹, Hao Zhu²

¹Department of Computer Science, University of Miami, Coral Gables, FL 33146, United States.

NAR Genomics and Bioinformatics

|January 28, 2025

Summary

This summary is machine-generated.

A new tool, scHiGex, predicts gene expression from single-cell Hi-C data. This computational method accurately captures cellular heterogeneity, aiding in cell type classification.

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Area of Science:

Biochemistry
Genomics
Computational Biology

Background:

Single-cell Hi-C experiments like HiRES capture chromosomal conformation and gene expression simultaneously.
Limited datasets exist for this novel technique, hindering comprehensive analysis.
A computational approach is needed to predict gene expression from existing single-cell Hi-C data.

Purpose of the Study:

To develop a computational tool for predicting single-cell gene expression levels using single-cell Hi-C data.
To validate the tool's accuracy and effectiveness in capturing cellular heterogeneity.

Main Methods:

Training a graph transformer model named scHiGex.
Utilizing single-cell Hi-C data as input for gene expression prediction.
Benchmarking scHiGex performance against established metrics.

Main Results:

scHiGex accurately predicts gene expression levels with a low average absolute error of 0.07.
Predicted gene expression successfully categorized cells into distinct types (Adjusted Rand Index score 1).
The model effectively captured inter-cell type heterogeneity.

Conclusions:

scHiGex is an effective computational tool for predicting gene expression from single-cell Hi-C data.
The model's ability to capture cellular heterogeneity opens new avenues for single-cell genomics research.
scHiGex is publicly available for the scientific community.