A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.
NAR Genom Bioinform
; 3(1): lqaa087, 2021 Mar.
Article
em En
| MEDLINE
| ID: mdl-33575647
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-cell RNA-seq data for human pancreatic islets. The practical merit is also demonstrated through comparisons with existing single-cell clustering methods. The R-package for the proposed method can be found at https//github.com/Zhang-Data-Science-Research-Lab/LrSClust.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article