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

被引:1
|
作者
Yanhong Wu [1 ]
Qifan Hu [1 ]
Shicheng Wang [1 ]
Changyi Liu [1 ]
Yiran Shan [1 ]
Wenbo Guo [1 ]
Rui Jiang [1 ]
Xiaowo Wang [1 ]
Jin Gu [1 ]
机构
[1] MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
Q811.4 [生物信息论];
学科分类号
0711 ; 0831 ;
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 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.
引用
收藏
页码:891 / 899
页数:9
相关论文
共 50 条
  • [31] Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks
    Mao, Guo
    Pang, Zhengbin
    Zuo, Ke
    Wang, Qinglin
    Pei, Xiangdong
    Chen, Xinhai
    Liu, Jie
    BRIEFINGS IN BIOINFORMATICS, 2023, 24 (06)
  • [32] GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data
    Kai Wang
    Yulong Li
    Fei Liu
    Xiaoli Luan
    Xinglong Wang
    Jingwen Zhou
    BMC Bioinformatics, 26 (1)
  • [33] scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data
    Wang, Zongqin
    Xie, Xiaojun
    Liu, Shouyang
    Ji, Zhiwei
    LIFE SCIENCE ALLIANCE, 2023, 6 (12)
  • [34] Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data
    Liu, Siyao
    Corcoran, David L.
    Garcia-Recio, Susana
    Marron, James S.
    Perou, Charles M.
    NAR GENOMICS AND BIOINFORMATICS, 2025, 7 (01)
  • [35] ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations
    Yu, Zhuohan
    Lu, Yifu
    Wang, Yunhe
    Tang, Fan
    Wong, Ka-Chun
    Li, Xiangtao
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 4671 - 4679
  • [36] Computational analysis of alternative polyadenylation from standard RNA-seq and single-cell RNA-seq data
    Gao, Yipeng
    Li, Wei
    MRNA 3' END PROCESSING AND METABOLISM, 2021, 655 : 225 - 243
  • [37] Analysis of Single-Cell RNA-seq Data by Clustering Approaches
    Zhu, Xiaoshu
    Li, Hong-Dong
    Guo, Lilu
    Wu, Fang-Xiang
    Wang, Jianxin
    CURRENT BIOINFORMATICS, 2019, 14 (04) : 314 - 322
  • [38] Evaluating imputation methods for single-cell RNA-seq data
    Yi Cheng
    Xiuli Ma
    Lang Yuan
    Zhaoguo Sun
    Pingzhang Wang
    BMC Bioinformatics, 24
  • [39] SCnorm: robust normalization of single-cell RNA-seq data
    Bacher, Rhonda
    Chu, Li-Fang
    Leng, Ning
    Gasch, Audrey P.
    Thomson, James A.
    Stewart, Ron M.
    Newton, Michael
    Kendziorski, Christina
    NATURE METHODS, 2017, 14 (06) : 584 - +
  • [40] Quantifying the clusterness and trajectoriness of single-cell RNA-seq data
    Lim, Hong Seo
    Qiu, Peng
    PLOS COMPUTATIONAL BIOLOGY, 2024, 20 (02)