Highly Regional Genes: graph-based gene selection for single-cell RNA-seq data.
IF: 5.723


Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq (scRNA-seq) data. Compared with the commonly used variance-based methods, by mimicking the human maker selection in the 2D visualization of cells, a new feature selection method called HRG (Highly Regional Genes) is proposed to find the informative genes, which show regional expression patterns in the cell-cell similarity network. We mathematically find the optimal expression patterns that can maximize the proposed scoring function. In comparison with several unsupervised methods, HRG shows high accuracy and robustness, and can increase the performance of downstream cell clustering and gene correlation analysis. Also, it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.


Spatial Transcriptomics
Feature selection
Graphical models
Regional patterns
Single-cell RNA-sequencing
Spatially resolved transcriptomic data

MeSH terms

Cluster Analysis
Gene Expression Profiling
Sequence Analysis, RNA
Single-Cell Analysis


Wu, Yanhong
Hu, Qifan
Wang, Shicheng
Liu, Changyi
Shan, Yiran
Guo, Wenbo
Jiang, Rui
Wang, Xiaowo
Gu, Jin

Recommend literature

Similar data