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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

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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Related Experiment Video

Updated: May 10, 2026

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

scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.

Ghulam Murtaza¹, Byron Butaney¹, Justin Wagner²

¹Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

Summary

This summary is machine-generated.

We developed scGrapHiC, a deep learning tool predicting single-cell Hi-C contact maps from scRNA-seq data. This method enhances the study of cell-type-specific chromatin interactions, overcoming limitations of current experimental protocols.

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Last Updated: May 10, 2026

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Published on: January 20, 2023

Area of Science:

Genomics and Bioinformatics
Computational Biology
Epigenetics

Background:

Single-cell Hi-C (scHi-C) reveals cell-type-specific chromatin interactions crucial for understanding cell differentiation and disease.
High cost and experimental complexity limit the widespread utilization of scHi-C data.
Existing methods often underutilize the rich information within scHi-C datasets.

Purpose of the Study:

To introduce scGrapHiC, a deep learning framework for predicting pseudo-bulk scHi-C contact maps from pseudo-bulk scRNA-seq data.
To enable the generation of cell-type-specific scHi-C contact maps using more accessible genomic signals.
To facilitate the study of genome-wide single-cell interactions and chromatin organization.

Main Methods:

scGrapHiC employs a graph deconvolution approach to infer single-cell interactions.
It utilizes scRNA-seq data as a guiding signal to extract information from bulk Hi-C contact maps.
The framework was trained and evaluated on seven cell-type co-assay datasets.

Main Results:

scGrapHiC outperforms traditional sequence encoder approaches in predicting scHi-C contact maps.
Achieved a 23.2% improvement in recovering cell-type-specific Topologically Associating Domains compared to baseline methods.
Demonstrated generalization capabilities on unseen embryo and brain tissue samples.

Conclusions:

scGrapHiC offers a novel and effective method for generating cell-type-specific scHi-C contact maps.
The framework democratizes the study of chromatin interactions by leveraging widely available scRNA-seq data.
Enables deeper insights into cell-type-specific genome organization and its role in biological processes.