Unit:
Κατεύθυνση Βιοπληροφορική-Υπολογιστική ΒιολογίαLibrary of the School of Science
Supervisors info:
Παντελής Μπάγκος, Καθηγητής, Τμήμα Πληροφορικής µε Εφαρμογές στην Βιοϊατρική, Πανεπιστήμιο Θεσσαλίας
Γεώργιος Παυλόπουλος, Ερευνητής B’, ΕΚΕΒΕ ‘Αλέξανδρος Φλέμινγκ’’
Βασιλική Οικονομίδου, Αναπληρώτρια Καθηγήτρια, Τομέας Βιολογίας Κυττάρου και Βιοφυσικής, Τμήμα Βιολογίας, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Original Title:
Οπτικοποίηση και συγκριτική ανάλυση κοινοτήτων σε βιοϊατρικά δίκτυα
Translated title:
Visualization and comparison of communities in biomedical networks
Summary:
Clustering is the process of grouping different data based on the similar properties they display. Clustering has applications in different studies related to various fields such as graph theory, image analysis, pattern recognition, statistics and more. Nowadays, there are many algorithms and tools capable of generating clustering results. However, different algorithms or different parameterization may result in very different clusters. This way, the user is often forced to manually filter and compare these results in order to decide which of them produce the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics application which allows the comparison and visualization of various clustering algorithms. VICTOR can handle multiple cluster results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared using interactive heatmaps, bar plots, correlation networks, sankey and circos plots. VICTOR’s functionality is demonstrated using three examples. In the first case, we compare five different algorithms on a protein-protein interaction dataset whereas in the second example, we test four different parameters of the same clustering algorithm applied on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction. VICTOR is available at http://bib.fleming.gr:3838/VICTOR.
Main subject category:
Science
Other subject categories:
Health Sciences
Keywords:
Cluster conductance, Cluster sets comparison, Counting pairs, Interactive visualization, Mutual information, Set overlaps
Number of references:
131
