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

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

doi:10.1016/j.jgg.2022.01.004

Volume 49 Issue 9

Sep. 2022

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Article Navigation > Journal of Genetics and Genomics > 2022 > 49(9): 891-899

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

doi: 10.1016/j.jgg.2022.01.004

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

Funds:

We address special thanks to the share of single-cell datasets by Hemberg group from the Wellcome Trust Sanger Institute. This work was supported by the National Key Research and Development Program (2020YFA0712403, 2020YFA0906900)

National Natural Science Foundation of China (61922047, 81890993, 61721003, 62133006) and BNRIST Young Innovation Fund (BNR2020RC01009).

Received Date: 2021-10-06
Accepted Date: 2022-01-25
Rev Recd Date: 2022-01-24
Publish Date: 2022-02-08

Abstract

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.
- Single-cell RNA-sequencing,
- Feature selection,
- Spatially resolved transcriptomic data,
- Regional patterns,
- Graphical models

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References(32)

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