Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells

• Published in: Journal of chemical information and modeling 2024-08, Vol.64 (16), p.6596-6609
• Main Authors: Wang, Shudong, Li, Hengxiao, Liu, Yahui, Pang, Shanchen, Qiao, Sibo, Su, Jionglong, Wang, Shaoqiang, Zhang, Yulin
• Format: Article
• Language: English
• Subjects: Computational Biochemistry; Datasets; Gene sequencing
• ISSN: 1549-9596; 1549-960X; 1549-960X
• DOI: 10.1021/acs.jcim.4c00796

Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells. SCLCNF creates a cellular connectivity network, considering how each cell relates to its neighbors. This network helps to pinpoint coexpression patterns unique to rare cells, utilizing a rarity score to confirm their presence. Our method performs better in detecting rare cells than existing techniques, offering enhanced robustness. It has proven to be effective in human gastrula data sets for accurately pinpointing rare cells, and in sepsis data sets where it uncovers previously unidentified rare cell populations.