Clustering methods are increasingly applied to single-cell DNA sequencing (scDNAseq) data to infer the subclonal structure of cancer. However, the complexity of these data exacerbates some data-science issues and affects clustering results. Additionally, determining whether such inferences are accurate and clusters recapitulate the real cell phylogeny is not trivial, mainly because ground truth information is not available for most experimental settings. Here, by exploiting simulated sequencing data representing known phylogenies of cancer cells, we propose a formal and systematic assessment of well-known clustering methods to study their performance and identify the approach providing the most accurate reconstruction of phylogenetic relationships.

Effective evaluation of clustering algorithms on single-cell CNA data / Montemurro, Marilisa; Urgese, Gianvito; Grassi, Elena; Pizzino, Carmelo Gabriele; Bertotti, Andrea; Ficarra, Elisa. - ELETTRONICO. - (2020). ((Intervento presentato al convegno ICBBE 2020 - 7th International Conference on Biomedical and Bioinformatics Engineering tenutosi a Kyoto nel 06-09 novembre 2020 [10.1145/3444884.3444886].

Effective evaluation of clustering algorithms on single-cell CNA data

Montemurro, Marilisa;Urgese, Gianvito;Ficarra, Elisa
2020

Abstract

Clustering methods are increasingly applied to single-cell DNA sequencing (scDNAseq) data to infer the subclonal structure of cancer. However, the complexity of these data exacerbates some data-science issues and affects clustering results. Additionally, determining whether such inferences are accurate and clusters recapitulate the real cell phylogeny is not trivial, mainly because ground truth information is not available for most experimental settings. Here, by exploiting simulated sequencing data representing known phylogenies of cancer cells, we propose a formal and systematic assessment of well-known clustering methods to study their performance and identify the approach providing the most accurate reconstruction of phylogenetic relationships.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2845484