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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

Identifying Gene Markers Associated with Cell Subpopulations.

Maria Luisa Ratto¹, Luca Alessandri²

¹Molecular Biotechnology Center, University of Torino, Turin, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|December 10, 2022

Summary

This study introduces a method using sparsely connected autoencoders and COMET to identify key genes that define cell subpopulations in single-cell RNA sequencing experiments. These tools convert cell clusters into pseudo-RNAseq data for differential expression analysis and marker gene visualization.

Keywords:

Comet Feature selection Sparsely connected autoencoder

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex datasets.
Identifying cell subpopulations and their defining genes is crucial for biological insight.
Current methods for marker gene identification can be computationally intensive.

Purpose of the Study:

To present a novel computational approach for identifying cell-subpopulation-specific gene markers.
To adapt single-cell data for downstream differential expression analysis.
To enhance the characterization of cellular heterogeneity.

Main Methods:

Utilized sparsely connected autoencoders to transform single-cell clusters into pseudo-RNA sequencing data.
Employed the COMET tool for the visualization and identification of cluster-specific gene markers.
Integrated these methods for a streamlined analysis pipeline.

Main Results:

Successfully converted single-cell cluster data into a format suitable for differential expression analysis.
Demonstrated the utility of COMET in effectively depicting key marker genes for distinct cell populations.
Provided a robust framework for gene marker discovery in scRNA-seq data.

Conclusions:

The described methodology offers an efficient way to identify characteristic genes of cell subpopulations.
Sparsely connected autoencoders and COMET are valuable tools for scRNA-seq data analysis.
This approach facilitates a deeper understanding of cellular heterogeneity and function.