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.

Computational Analysis of Single-Cell RNA-Seq Data

Alessandri L.;Beccuti M.;Arigoni M.;Calogero R. A.
2021

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.
Methods in Molecular Biology
Humana Press Inc.
2284
289
301
978-1-0716-1306-1
978-1-0716-1307-8
Bioinformatics; Cell markers; Clustering; Droplet; Single cell RNA sequencing; Spatial transcriptomics; Cluster Analysis; Computational Biology; Datasets as Topic; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; RNA-Seq; Sequence Analysis, RNA; Single-Cell Analysis; Whole Exome Sequencing
Alessandri L.; Cordero F.; Beccuti M.; Arigoni M.; Calogero R.A.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1829647
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