LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes

Qiaoming Liu; Yingjian Liang; Dong Wang; Jie Li

doi:10.3389/fgene.2022.1068075

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes

Front Genet. 2022 Dec 1:13:1068075. doi: 10.3389/fgene.2022.1068075. eCollection 2022.

Authors

Qiaoming Liu¹, Yingjian Liang^{2

3}, Dong Wang¹, Jie Li¹

Affiliations

¹ School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
² Department of General Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
³ Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China.

Abstract

The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk RNA sequencing data to identify cell types from single-cell transcriptomes. An anchor graph is constructed to depict the relationship between reference samples and cells. By applying a connectivity constraint to the learned graph, LFSC enables the preservation of the underlying cluster structure. Moreover, the overall complexity of LFSC is linear to the size of the data, which greatly improves effectiveness and efficiency. By applying LFSC to real single-cell RNA sequencing datasets, we discovered that it has superior performance over existing baseline methods in clustering accuracy and robustness. An application using infiltrating T cells in liver cancer demonstrates that LFSC can successfully find new cell types, discover differently expressed genes, and explore new cancer-associated biomarkers.

Keywords: anchor graph; bulk RNA-seq; clustering; data integration; single-cell RNA-seq.