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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


© 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

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.

Li-2023-CellBRFAFeatureSupplementaryMaterials.pdf (1679 kB)
Supplementary Materials