A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data

Xin Yuan; Shuangge Ma; Botao Fa; Ting Wei; Yanran Ma; Yifan Wang; Wenwen Lv; Yue Zhang; Junke Zheng; Guoqiang Chen; Jing Sun; Zhangsheng Yu

doi:10.3389/fgene.2022.1063130

A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data

Front Genet. 2022 Nov 29:13:1063130. doi: 10.3389/fgene.2022.1063130. eCollection 2022.

Authors

Xin Yuan^{1

2}, Shuangge Ma^{2

3}, Botao Fa⁴, Ting Wei^{1

2}, Yanran Ma^{1

2}, Yifan Wang¹, Wenwen Lv⁵, Yue Zhang^{1

2}, Junke Zheng⁶, Guoqiang Chen⁷, Jing Sun⁸, Zhangsheng Yu^{1

2

5

9}

Affiliations

¹ Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
² SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Biostatistics, Yale University, New Haven, CT, United States.
⁴ Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, China.
⁵ Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁷ State Key Laboratory of Oncogene and Related Gene, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁸ Shanghai Minimally Invasive Surgery Center, Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁹ Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy ( $F_{1}$ score >0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts ( $F_{1}$ score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.

Keywords: DE gene; PBMC68K; colorectal cancer; combination test; differential analysis.