典型文献
Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data
文献摘要：
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 hu-man 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.
文献关键词：
中图分类号：
作者姓名：
Yanhong Wu;Qifan Hu;Shicheng Wang;Changyi Liu;Yiran Shan;Wenbo Guo;Rui Jiang;Xiaowo Wang;Jin Gu
作者机构：
MOE Key Laboratory of Bioinformatics,BNRIST Bioinformatics Division,Department of Automation,Tsinghua University,Beijing 100084,China
文献出处：
引用格式：
[1]Yanhong Wu;Qifan Hu;Shicheng Wang;Changyi Liu;Yiran Shan;Wenbo Guo;Rui Jiang;Xiaowo Wang;Jin Gu-.Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data)[J].遗传学报,2022(09):891-899
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AB值：
0.594481
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