The multilayer community structure of medulloblastoma
Summary: Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to ide...
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2021-04-01
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doaj-c4d73bcd932c4c1eac16b7d6031b0f2d2021-04-26T05:58:02ZengElsevieriScience2589-00422021-04-01244102365The multilayer community structure of medulloblastomaIker Núñez-Carpintero0Marianyela Petrizzelli1Andrei Zinovyev2Davide Cirillo3Alfonso Valencia4Barcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, SpainInstitut Curie, PSL Research University, 75005 Paris, France; INSERM, U900, 75005 Paris, France; MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, FranceInstitut Curie, PSL Research University, 75005 Paris, France; INSERM, U900, 75005 Paris, France; MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, France; Lobachevsky University, 603000 Nizhny Novgorod, RussiaBarcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain; Corresponding authorBarcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain; ICREA - Institució Catalana de Recerca i Estudis Avançats, Pg. Lluís Companys 23, 08010, Barcelona, SpainSummary: Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets.http://www.sciencedirect.com/science/article/pii/S2589004221003333ProteomicsCancer Systems BiologyCancer |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Iker Núñez-Carpintero Marianyela Petrizzelli Andrei Zinovyev Davide Cirillo Alfonso Valencia |
spellingShingle |
Iker Núñez-Carpintero Marianyela Petrizzelli Andrei Zinovyev Davide Cirillo Alfonso Valencia The multilayer community structure of medulloblastoma iScience Proteomics Cancer Systems Biology Cancer |
author_facet |
Iker Núñez-Carpintero Marianyela Petrizzelli Andrei Zinovyev Davide Cirillo Alfonso Valencia |
author_sort |
Iker Núñez-Carpintero |
title |
The multilayer community structure of medulloblastoma |
title_short |
The multilayer community structure of medulloblastoma |
title_full |
The multilayer community structure of medulloblastoma |
title_fullStr |
The multilayer community structure of medulloblastoma |
title_full_unstemmed |
The multilayer community structure of medulloblastoma |
title_sort |
multilayer community structure of medulloblastoma |
publisher |
Elsevier |
series |
iScience |
issn |
2589-0042 |
publishDate |
2021-04-01 |
description |
Summary: Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared with the size of the associated multi-omics datasets. In this work, we develop a dimensionality reduction methodology to identify the minimal set of genes that characterize disease subgroups based on their persistent association in multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets. |
topic |
Proteomics Cancer Systems Biology Cancer |
url |
http://www.sciencedirect.com/science/article/pii/S2589004221003333 |
work_keys_str_mv |
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1721507856842227712 |