高度区域性基因：基于图的单细胞 RNA-seq 数据基因选择。

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

机构信息

MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.

出版信息

J Genet Genomics. 2022 Sep;49(9):891-899. doi: 10.1016/j.jgg.2022.01.004. Epub 2022 Feb 8.

DOI:10.1016/j.jgg.2022.01.004

PMID:35144027

Abstract

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.

摘要

基因选择是分析嘈杂和高维的单细胞 RNA-seq（scRNA-seq）数据所不可或缺的步骤。与常用的基于方差的方法相比，通过模拟细胞 2D 可视化中的人类标记物选择，提出了一种新的特征选择方法称为 HRG（高区域基因），用于找到在细胞细胞相似性网络中表现出区域表达模式的信息基因。我们从数学上找到了可以最大化所提出的评分函数的最佳表达模式。与几种无监督方法相比，HRG 具有高精度和鲁棒性，并可以提高下游细胞聚类和基因相关性分析的性能。此外，它还适用于选择基于测序的空间转录组学数据中的信息基因。