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RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest. | LitMetric

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest.

Front Genet

Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.

Published: July 2021

AI Article Synopsis

Article Abstract

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354212PMC
http://dx.doi.org/10.3389/fgene.2021.665843DOI Listing

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