DCAE: Selecting Discriminative Genes on Single-cell RNA-seq Data for Cell-type Quantification

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Tumor-liltrating lymphocytes (TILs) are predictive for response to neoadjuvant treatment in tumors. Still, the abundance of tumor-liltrating cell types has not yet been produced in large quantities, hampering researchers exploring their characteristics. As the levels of genomics or transcriptomics could reflect changes in cell-type proportions, several computational tools have been developed to estimate cell-type abundances based on the reference gene expression proliles. Differential expression analysis is the most widely used to recognize marker genes. However, it ignores the correlation between genes. To this end, we propose a feature selection method, dubbed Discriminative Concrete Autoencoder (DCAE), to identify informative genes on single-cell RNA-seq data, which are then used to quantity cell-type proportions. To evaluate the performance of DCAE on selecting discriminative genes, we conduct experiments on our collected and processed single-cell RNA-seq dataset. First, we compare DCAE to the original Concrete Autoencoder by the cell-type classification accuracies resulting from their selected genes. Then we infer cell-type abundance by using deconvolution function with the chosen small cohort of genes. Next, we evaluate the deconvolution accuracy by the Pearson correlation coefficient between the estimated cell-type proportions and the true proportions, and the corresponding P-value. Finally, we compare the effects of the selected genes and the differential expression genes on the deconvolution accuracy. The results show that our selected genes by DCAE have higher discriminant power to distinguish cell types and effectively infer cell-type abundance. Thus, DCAE provides insights into acquiring candidate biomarkers for cell-type quantification.

源语言英语
主期刊名Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
编辑Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
出版商Institute of Electrical and Electronics Engineers Inc.
1865-1872
页数8
ISBN(电子版)9781665401265
DOI
出版状态已出版 - 2021
活动2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, 美国
期限: 9 12月 202112 12月 2021

出版系列

姓名Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

会议

会议2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
国家/地区美国
Virtual, Online
时期9/12/2112/12/21

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