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

Abstract Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering...

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Bibliographic Details
Published in:Briefings in bioinformatics 2023-09, Vol.24 (6)
Main Authors: Zhang, Chao, Duan, Zhi-Wei, Xu, Yun-Pei, Liu, Jin, Li, Hong-Dong
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Datasets
Decomposition
Feature selection
Feeds
Gene expression
Genes
Heterogeneity
Mathematical analysis
Permutations
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Summary:Abstract Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbad389