scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.
Minjiao Peng1,2, Baoqin Lin3, Jun Zhang1
1School of Mathematical Sciences, Shenzhen University, Nanshan, Shenzhen, 518060, Guangdong, China.
BMC Genomics
|March 8, 2024
Summary
This study introduces scFSNN, a novel neural network method for feature selection in single-cell RNA sequencing (scRNA-seq) data. It effectively identifies relevant genes for cell classification, overcoming challenges posed by complex scRNA-seq characteristics.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data.
- scRNA-seq data presents unique challenges including over-dispersion, zero-inflation, and high dimensionality.
- Existing feature selection methods struggle with the complexity of scRNA-seq data.
Purpose of the Study:
- To develop a robust feature selection method tailored for scRNA-seq data.
- To address the challenges of high dimensionality and complex data characteristics in scRNA-seq.
- To improve classification performance on scRNA-seq datasets.
Main Methods:
- A novel feature selection method based on neural networks, termed scFSNN, was developed.
- scFSNN is an embedded method that performs feature selection during model training.
- The method incorporates automatic feature selection, false discovery rate control, and adaptive feature elimination.
Main Results:
- scFSNN demonstrated superior feature selection capabilities compared to existing methods.
- The method achieved excellent predictive performance in classification tasks.
- Extensive simulations and real-world data analyses validated scFSNN's effectiveness.
Conclusions:
- scFSNN offers an effective solution for feature selection in scRNA-seq data analysis.
- The method enhances the accuracy and reliability of cell classification from scRNA-seq data.
- scFSNN provides a valuable tool for researchers working with complex single-cell genomics datasets.
