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scFSNN: a feature selection method based on neural network for single-cell RNA-seq data

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机构： [1]School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China. [2]School of Mathematics and Statistics and KLAS, Northeast Normal University, Renmin Street, Changchun, 130000, Jilin, China. [3]Experimental Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, China

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关键词： Feature selection Deep neural network FDR control

摘要：

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.© 2024. The Author(s).

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出版当年[2023]版：

大类 | 2 区

生物学

小类 | 2 区生物工程与应用微生物 2 区遗传学

最新[2025]版：

大类 | 2 区生物学

小类 | 2 区生物工程与应用微生物 3 区遗传学

第一作者：

第一作者机构： [1]School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China. [2]School of Mathematics and Statistics and KLAS, Northeast Normal University, Renmin Street, Changchun, 130000, Jilin, China.

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