A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis.
Brief Bioinform
; 24(2)2023 03 19.
Article
em En
| MEDLINE
| ID: mdl-36754847
ABSTRACT
Feature gene selection has significant impact on the performance of cell clustering in single-cell RNA sequencing (scRNA-seq) analysis. A well-rounded feature selection (FS) method should consider relevance, redundancy and complementarity of the features. Yet most existing FS methods focus on gene relevance to the cell types but neglect redundancy and complementarity, which undermines the cell clustering performance. We develop a novel computational method GeneClust to select feature genes for scRNA-seq cell clustering. GeneClust groups genes based on their expression profiles, then selects genes with the aim of maximizing relevance, minimizing redundancy and preserving complementarity. It can work as a plug-in tool for FS with any existing cell clustering method. Extensive benchmark results demonstrate that GeneClust significantly improve the clustering performance. Moreover, GeneClust can group cofunctional genes in biological process and pathway into clusters, thus providing a means of investigating gene interactions and identifying potential genes relevant to biological characteristics of the dataset. GeneClust is freely available at https//github.com/ToryDeng/scGeneClust.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Perfilação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China