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.

Single-Cell RNAseq Clustering

Beccuti M.;Calogero R. A.
2023-01-01

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.
2023
Methods in Molecular Biology
Humana Press Inc.
2584
241
250
978-1-0716-2755-6
978-1-0716-2756-3
Griph; Lovain modularity; Seurat; SHARP; Single cell transcriptomics; Unsupervised clustering
Beccuti M.; Calogero R.A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1885421
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