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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Selecting gene features for unsupervised analysis of single-cell gene expression data.

Jie Sheng¹, Wei Vivian Li²

¹Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA.

Briefings in Bioinformatics

|August 5, 2021

Summary

This study systematically compares 17 gene selection methods for single-cell RNA sequencing (scRNA-seq) data. It aids researchers in choosing appropriate tools for analyzing gene expression and developing new computational methods for scRNA-seq analysis.

Keywords:

feature selection highly variable genes single-cell genomics unsupervised learning

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Last Updated: Oct 25, 2025

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptomic data.
Gene feature selection is crucial for analyzing scRNA-seq data, identifying biologically variable genes, and reducing complexity.
Existing gene selection methods for scRNA-seq lack systematic comparison of their underlying assumptions and criteria.

Purpose of the Study:

To systematically review and compare 17 computational methods for gene feature selection in unsupervised scRNA-seq analysis.
To provide a unified framework for understanding the assumptions, statistical models, and selection criteria of these methods.
To guide practitioners in selecting appropriate methods and assist developers in creating new tools.

Main Methods:

Literature review and systematic comparison of 17 gene selection algorithms.
Development of unified notations and statistical frameworks for method evaluation.
Analysis of assumptions, statistical models, and selection criteria for each method.

Main Results:

A comprehensive summary and discussion of 17 distinct gene selection methods.
Identification of variations in assumptions, statistical models, and selection criteria across methods.
A framework for evaluating the applicability and performance of different gene selection techniques.

Conclusions:

A systematic comparison of gene selection methods is essential for scRNA-seq data analysis.
Understanding method assumptions and criteria aids in selecting the most suitable approach.
This work facilitates informed choices for researchers and developers in the field of single-cell transcriptomics.