eprintid: 10097890 rev_number: 15 eprint_status: archive userid: 608 dir: disk0/10/09/78/90 datestamp: 2020-05-20 10:14:54 lastmod: 2021-10-08 21:42:58 status_changed: 2020-05-20 10:14:54 type: article metadata_visibility: show creators_name: Windels, SFL creators_name: Malod-Dognin, N creators_name: Przulj, N title: Graphlet Laplacians for topology-function and topology-disease relationships ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Motivation: Laplacian matrices capture the global structure of networks and are widely used to study biological networks. However, the local structure of the network around a node can also capture biological information. Local wiring patterns are typically quantified by counting how often a node touches different graphlets (small, connected, induced sub-graphs). Currently available graphlet-based methods do not consider whether nodes are in the same network neighbourhood. To combine graphlet-based topological information and membership of nodes to the same network neighbourhood, we generalize the Laplacian to the Graphlet Laplacian, by considering a pair of nodes to be ‘adjacent’ if they simultaneously touch a given graphlet. Results: We utilize Graphlet Laplacians to generalize spectral embedding, spectral clustering and network diffusion. Applying Graphlet Laplacian-based spectral embedding, we visually demonstrate that Graphlet Laplacians capture biological functions. This result is quantified by applying Graphlet Laplacian-based spectral clustering, which uncovers clusters enriched in biological functions dependent on the underlying graphlet. We explain the complementarity of biological functions captured by different Graphlet Laplacians by showing that they capture different local topologies. Finally, diffusing pan-cancer gene mutation scores based on different Graphlet Laplacians, we find complementary sets of cancer-related genes. Hence, we demonstrate that Graphlet Laplacians capture topology-function and topology-disease relationships in biological networks. Availability and implementation: http://www0.cs.ucl.ac.uk/staff/natasa/graphlet-laplacian/index.html date: 2019-12-15 date_type: published publisher: OXFORD UNIV PRESS official_url: http://dx.doi.org/10.1093/bioinformatics/btz455 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1662536 doi: 10.1093/bioinformatics/btz455 lyricists_name: Przulj, Natasa lyricists_id: NPRZU85 actors_name: Dewerpe, Marie actors_id: MDDEW97 actors_role: owner full_text_status: public publication: Bioinformatics volume: 35 number: 24 pagerange: 5226-5234 pages: 9 citation: Windels, SFL; Malod-Dognin, N; Przulj, N; (2019) Graphlet Laplacians for topology-function and topology-disease relationships. Bioinformatics , 35 (24) pp. 5226-5234. 10.1093/bioinformatics/btz455 <https://doi.org/10.1093/bioinformatics%2Fbtz455>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10097890/1/Przulj_main_revision_submitted_noformat.pdf