A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.

ABSTRACT: Traditional bulk RNA-sequencing of human pancreatic islets mainly reflects transcriptional response of major cell types. Single-cell RNA sequencing technology enables transcriptional characterization of individual cells, and thus makes it possible to detect cell types and subtypes. To tackle the heterogeneity of single-cell RNA-seq data, powerful and appropriate clustering is required to facilitate the discovery of cell types. In this paper, we propose a new clustering framework based on a graph-based model with various types of dissimilarity measures. We take the compositional nature of single-cell RNA-seq data into account and employ log-ratio transformations. The practical merit of the proposed method is demonstrated through the application to the centered log-ratio-transformed single-c

SUBMITTER: Wu H 

PROVIDER: S-EPMC7803008 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature