Single-Cell RNAseq Clustering

Marco Beccuti; Raffaele A Calogero

doi:10.1007/978-1-0716-2756-3_12

Single-Cell RNAseq Clustering

Methods Mol Biol. 2023:2584:241-250. doi: 10.1007/978-1-0716-2756-3_12.

Authors

Marco Beccuti¹, Raffaele A Calogero²

Affiliations

¹ Department of Computer Science, University of Torino, Turin, Italy. marco.beccuti@unito.it.
² Molecular Biotechnology Center, University of Torino, Torino, Italy.

PMID: 36495454
DOI: 10.1007/978-1-0716-2756-3_12

Abstract

Single-cell RNA sequencing (scRNA-seq) allows the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation organization of a sample. In this chapter, we describe how to approach the clustering of single-cell RNAseq transcriptomics data using various clustering tools, and we provide some information on the limitations affecting the clustering procedure.

Keywords: Griph; Lovain modularity; SHARP; Seurat; Single cell transcriptomics; Unsupervised clustering.

MeSH terms

Algorithms
Cluster Analysis
Gene Expression Profiling / methods
Sequence Analysis, RNA / methods
Single-Cell Analysis* / methods
Single-Cell Gene Expression Analysis*