scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.
Minjiao Peng, Baoqin Lin, Jun Zhang, Yan Zhou, Bingqing Lin
Author Information
Minjiao Peng: School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China.
Baoqin Lin: Experimental Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, China.
Jun Zhang: School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China.
Yan Zhou: School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China.
Bingqing Lin: School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China. bqlin@szu.edu.cn.
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose challenges for most existing feature selection methods. In this paper, we present a feature selection method based on neural network (scFSNN) to solve classification problem for the scRNA-seq data. scFSNN is an embedded method that can automatically select features (genes) during model training, control the false discovery rate of selected features and adaptively determine the number of features to be eliminated. Extensive simulation and real data studies demonstrate its excellent feature selection ability and predictive performance.
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12071305/National Natural Science Foundation of China
11701386/National Natural Science Foundation of China
2023A1515011399/Natural Science Foundation of Guangdong Province of China
Single-Cell Gene Expression Analysis
Neural Networks, Computer
Computer Simulation
Single-Cell Analysis
Sequence Analysis, RNA
Gene Expression Profiling
Cluster Analysis
