Abstract 4410: Automatic detection of consensus gene clusters across multiple single-cell datasets

Abstract

Abstract Identifying co-expressed gene clusters can provide evidence for cellular activities. Thus, co-expression clustering will be a routine step in single-cell RNA-seq data analysis. We show that commonly used clustering methods for bulk-seq data are not suitable for single-cell data, and produce results that substantially disagree in the biological expectations of co-expressed genes. Herein, we present scLM, a tailored method for single-cell data, to extract co-expressed gene clusters matching the biological expectations and outperform widely used methods. Additionally, scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage the large quantity of public data for novel comparative analysis. ScLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and real experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-Seq datasets. ScLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. Citation Format: Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang. Automatic detection of consensus gene clusters across multiple single-cell datasets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4410.