聚类分析
可解释性
特征选择
计算机科学
特征(语言学)
鉴定(生物学)
水准点(测量)
随机森林
选择(遗传算法)
人工智能
数据挖掘
机器学习
生物
哲学
语言学
植物
大地测量学
地理
作者
Yunpei Xu,Hong‐Dong Li,Cui-Xiang Lin,Ruiqing Zheng,Yaohang Li,Jinhui Xu,Jianxin Wang
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2023-06-01
卷期号:39 (Supplement_1): i368-i376
被引量:5
标识
DOI:10.1093/bioinformatics/btad216
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI