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Related Experiment Video

Updated: May 25, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Enhanced single-cell RNA-seq embedding through gene expression and data-driven gene-gene interaction integration.

Hojjat Torabi Goudarzi¹, Maziyar Baran Pouyan²

¹Electrical Engineering and Computer Science Department, Oregon State University, Address one, Corvallis, 97331, OR, United States.

Computers in Biology and Medicine

|February 25, 2025

Summary

This summary is machine-generated.

This study introduces a new method for single-cell RNA sequencing (scRNA-seq) analysis that integrates gene expression and gene interactions. This approach improves the identification of rare cell populations and enhances downstream analyses for better understanding cellular diversity.

Keywords:

Cell embedding Data-driven gene-gene interaction Gene expression Graph neural network Similarity learning Single-cell RNA-seq

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular heterogeneity.
scRNA-seq data presents analytical challenges due to high dimensionality and technical noise.
Existing embedding methods often neglect gene-gene interactions crucial for cellular identity.

Purpose of the Study:

To develop a novel embedding approach for scRNA-seq data that integrates gene expression profiles and gene-gene interactions.
To create a more comprehensive representation of cellular states by incorporating regulatory relationships.
To improve the detection of rare cell populations and downstream analysis performance.

Main Methods:

Constructed a Cell-Leaf Graph (CLG) using random forest models to capture gene regulatory relationships.
Built a K-Nearest Neighbor Graph (KNNG) to represent cell expression similarities.
Combined CLG and KNNG into an Enriched Cell-Leaf Graph (ECLG) for graph neural network-based cell embedding.

Main Results:

The proposed method provides a more comprehensive cell embedding by integrating expression levels and gene-gene interactions.
Demonstrated enhanced detection of rare cell populations across multiple datasets.
Showed improved performance in downstream analyses including visualization, clustering, and trajectory inference.

Conclusions:

The novel embedding approach offers a significant advancement in scRNA-seq data analysis.
Integrating gene expression and gene-gene interactions provides a more complete framework for studying cellular diversity and dynamics.
This method enhances the analytical capabilities for complex biological systems at single-cell resolution.