NDLI: Redefining the high variable genes by optimized LOESS regression with positive ratio

Content Provider	Springer Nature : BioMed Central
Author	Xie, Yue Jing, Zehua Pan, Hailin Xu, Xun Fang, Qi
Abstract	Background Single-cell RNA sequencing allows for the exploration of transcriptomic features at the individual cell level, but the high dimensionality and sparsity of the data pose substantial challenges for downstream analysis. Feature selection, therefore, is a critical step to reduce dimensionality and enhance interpretability. Results We developed a robust feature selection algorithm that leverages optimized locally estimated scatterplot smoothing regression (LOESS) to precisely capture the relationship between gene average expression level and positive ratio while minimizing overfitting. Our evaluations showed that our algorithm consistently outperforms eight leading feature selection methods across three benchmark criteria and helps improve downstream analysis, thus offering a significant improvement in gene subset selection. Conclusions By preserving key biological information through feature selection, GLP provides informative features to enhance the accuracy and effectiveness of downstream analyses.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-025-06112-5.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-025-06112-5
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	26
Language	English
Publisher	BioMed Central
Publisher Date	2025-04-15
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Single cell transcriptome High variable genes Feature selection Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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