Computational Analysis of Single-Cell RNA-Seq Data

Luca Alessandrì; Francesca Cordero; Marco Beccuti; Maddalena Arigoni; Raffaele A Calogero

doi:10.1007/978-1-0716-1307-8_16

Computational Analysis of Single-Cell RNA-Seq Data

Methods Mol Biol. 2021:2284:289-301. doi: 10.1007/978-1-0716-1307-8_16.

Authors

Luca Alessandrì¹, Francesca Cordero², Marco Beccuti², Maddalena Arigoni¹, Raffaele A Calogero³

Affiliations

¹ Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.
² Department of Computer Science, University of Torino, Torino, Italy.
³ Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy. raffaele.calogero@unito.it.

PMID: 33835449
DOI: 10.1007/978-1-0716-1307-8_16

Abstract

Single-cell RNAseq data can be generated using various technologies, spanning from isolation of cells by FACS sorting or droplet sequencing, to the use of frozen tissue sections retaining spatial information of cells in their morphological context. The analysis of single cell RNAseq data is mainly focused on the identification of cell subpopulations characterized by specific gene markers that can be used to purify the population of interest for further biological studies. This chapter describes the steps required for dataset clustering and markers detection using a droplet dataset and a spatial transcriptomics dataset.

Keywords: Bioinformatics; Cell markers; Clustering; Droplet; Single cell RNA sequencing; Spatial transcriptomics.

MeSH terms

Cluster Analysis
Computational Biology / methods*
Datasets as Topic
Exome Sequencing / methods
Gene Expression Profiling / methods
High-Throughput Nucleotide Sequencing / methods
Humans
RNA-Seq / methods*
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods*