Single-Cell RNAseq Clustering
- PMID: 36495454
- DOI: 10.1007/978-1-0716-2756-3_12
Single-Cell RNAseq Clustering
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.
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
References
-
- Kiselev VY, Andrews TS, Hemberg M (2019) Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet 20(5):273–282. https://doi.org/10.1038/s41576-018-0088-9 - DOI
-
- Serra D, Mayr U, Boni A, Lukonin I, Rempfler M, Challet Meylan L, Stadler MB, Strnad P, Papasaikas P, Vischi D, Waldt A, Roma G, Liberali P (2019) Self-organization and symmetry breaking in intestinal organoid development. Nature 569(7754):66–72. https://doi.org/10.1038/s41586-019-1146-y - DOI
-
- Wan S, Kim J, Won KJ (2020) SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection. Genome Res 30(2):205–213. https://doi.org/10.1101/gr.254557.119 - DOI
-
- Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420. https://doi.org/10.1038/nbt.4096 - DOI
-
- Alessandri L, Cordero F, Beccuti M, Arigoni M, Olivero M, Romano G, Rabellino S, Licheri N, De Libero G, Pace L, Calogero RA (2019) rCASC: reproducible classification analysis of single-cell sequencing data. Gigascience 8(9):giz105. https://doi.org/10.1093/gigascience/giz105 - DOI
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