Presenter: Marilisa Montemurro
Monday, November 30th, 2020 17:30
Location: Microsoft Teams – click here to join
Marilisa Montemurro: Effective Evaluation of Clustering Algorithms on Single-Cell CNA data
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.
Biography: I received the Master’s Degree in Computer Engineering at Politecnico di Torino, where I am, currently, a Computer and Control Engineering Ph.D. student, at the Interdipartimental Centre for SmartData. My interests are mainly in the field of Artificial Intelligence, Bioinformatics and Data Mining and Analysis. Currently, I am working to develop a mathematical model to describe cancer evolution by means of Artificial Intelligence-based techniques applied to temporal and spatial molecular data.