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FEED: a feature selection method based on gene expression decomposition for single cell clustering.

Chao Zhang¹, Zhi-Wei Duan¹, Yun-Pei Xu¹

¹School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China.

Briefings in Bioinformatics

|November 7, 2023

Summary

This summary is machine-generated.

Feature selection using gene expression decomposition (FEED) improves single-cell RNA sequencing (scRNA-seq) data clustering. This method enhances cell-type identification accuracy by analyzing gene expression distributions.

Keywords:

gene expression gene selection scRNA-seq single-cell clustering

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) analysis relies on accurate clustering for downstream biological interpretation.
Current feature selection methods often overlook gene expression distribution and intra-group heterogeneity, limiting clustering performance.
Identifying cell-type-specific genes is crucial for improving the precision of single-cell data analysis.

Purpose of the Study:

To introduce a novel feature selection method, Feature sElection based on gene Expression Decomposition (FEED), for scRNA-seq data.
To enhance the accuracy of cell-type identification in scRNA-seq datasets by selecting informative genes.
To address limitations of existing methods by incorporating gene expression distribution and heterogeneity.

Main Methods:

Decomposition of gene expression levels into multiple Gaussian components.
A novel gene correlation calculation method based on expression distribution.
A permutation-based approach to determine gene importance thresholds for marker gene subset selection.

Main Results:

FEED significantly improves cell-type identification accuracy across various scRNA-seq datasets, including large-scale ones.
The method demonstrates superior performance compared to state-of-the-art feature selection techniques.
Application of FEED followed by common clustering algorithms consistently yields better results.

Conclusions:

FEED offers a robust approach for feature selection in scRNA-seq data analysis.
The method effectively leverages gene expression distribution to identify informative genes.
FEED enhances the reliability and accuracy of single-cell data clustering and cell-type identification.