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Updated: Jun 28, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

Yulia I Nussbaum¹, K S M Tozammel Hossain², Jussuf Kaifi³

¹Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65201, USA.

Journal of Biomedical Informatics

|April 17, 2024

Summary

This summary is machine-generated.

This study introduces linear CorEx, a machine learning method that identifies gene expression programs (GEPs) linked to cell types and biological activities from single-cell RNA sequencing data, improving gene regulation insights.

Keywords:

Developmental biology Machine learning Single cell Transfer learning scRNA-seq

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Published on: October 25, 2018

Area of Science:

Genomics and bioinformatics
Computational biology
Molecular biology

Background:

Single-cell RNA sequencing (scRNA-seq) advances gene regulation studies.
Current methods primarily identify cell type-specific gene expression programs (GEPs).
Characterizing GEPs for biological processes and stimuli responses is limited.

Purpose of the Study:

To infer biologically meaningful GEPs from scRNA-seq data.
To associate GEPs with cellular phenotypes and activity programs.
To develop a robust method for analyzing complex scRNA-seq signals.

Main Methods:

Applied linear CorEx, a machine learning approach, to group genes based on total correlation optimization.
Utilized simulated and real-world scRNA-seq datasets for GEP inference.
Employed transfer learning to project inferred GEPs across datasets.

Main Results:

Linear CorEx outperformed similar methods in identifying cell types and activity programs on simulated data.
Identified biologically relevant GEPs in mouse dentate gyrus and embryonic colon development data.
Demonstrated cross-species sensitivity and transfer learning potential of linear CorEx.

Conclusions:

Linear CorEx is a valuable tool for comprehensive scRNA-seq data analysis.
Provides deeper insights into gene expression dynamics and cellular heterogeneity.
Enhances understanding of regulatory mechanisms in complex biological systems.