Document Type
Article
Publication Date
2023
DOI
10.1093/bioinformatics/btad216
Publication Title
Bioinformatics
Volume
39
Issue
Supplement 1
Pages
i368-i376
Abstract
Motivation
Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results
We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. Availability and implementation
All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.
Rights
© The Authors 2023.
Published under a Creative Commons Attribution 4.0 International (CC BY 4.0).
Data Availability
Article states: All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF.
Supplementary data is available at Bioinformatics online.
Original Publication Citation
Xu, Y., Li, H. D., Lin, C. X., Zheng, R., Li, Y., Xu, J., & Wang, J. (2023). CellBRF: A feature selection method for single-cell clustering using cell balance and random forest. Bioinformatics, 39 (1 Suppl.) i368-i376. https://doi.org/10.1093/bioinformatics/btad216
Repository Citation
Xu, Y., Li, H. D., Lin, C. X., Zheng, R., Li, Y., Xu, J., & Wang, J. (2023). CellBRF: A feature selection method for single-cell clustering using cell balance and random forest. Bioinformatics, 39 (1 Suppl.) i368-i376. https://doi.org/10.1093/bioinformatics/btad216
Supplementary Materials