Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.

ABSTRACT:

Background

Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.

Results

In this work, we explore the utility of various deep learning-based feature selection methods for s

SUBMITTER: Huang H 

PROVIDER: S-EPMC10638755 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature