MONET: Multi-omic module discovery by omic selection.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
28
04
2020
accepted:
22
07
2020
revised:
25
09
2020
pubmed:
16
9
2020
medline:
21
10
2020
entrez:
15
9
2020
Statut:
epublish
Résumé
Recent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present MONET (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. MONET discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most existent multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures. We tested MONET extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that MONET compares favorably with other multi-omic clustering methods. We demonstrate MONET's biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that MONET is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that MONET is a valuable tool that can provide complementary results to those provided by existent algorithms for multi-omic analysis.
Identifiants
pubmed: 32931516
doi: 10.1371/journal.pcbi.1008182
pii: PCOMPBIOL-D-20-00697
pmc: PMC7518594
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1008182Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Br J Haematol. 1976 Aug;33(4):451-8
pubmed: 188440
Nature. 2011 Jun 29;474(7353):609-15
pubmed: 21720365
PLoS Comput Biol. 2011 Oct;7(10):e1002227
pubmed: 22028636
Nat Methods. 2014 Mar;11(3):333-7
pubmed: 24464287
Cell Rep. 2017 Aug 1;20(5):1215-1228
pubmed: 28768204
Biometrics. 2003 Dec;59(4):1151-7
pubmed: 14969496
Genome Biol. 2020 May 11;21(1):111
pubmed: 32393329
Lancet Oncol. 2016 Feb;17(2):e81-e86
pubmed: 26868357
BMC Syst Biol. 2007 Jan 26;1:8
pubmed: 17408515
Bioinformatics. 2012 Dec 15;28(24):3290-7
pubmed: 23047558
Breast Cancer Res. 2016 Jul 07;18(1):74
pubmed: 27386846
Nature. 2019 Dec;576(7787):487-491
pubmed: 31827285
Nature. 2008 Oct 23;455(7216):1061-8
pubmed: 18772890
Nat Rev Genet. 2013 Mar;14(3):204-20
pubmed: 23400093
Bioinformatics. 2019 Sep 15;35(18):3348-3356
pubmed: 30698637
Front Oncol. 2015 Nov 02;5:245
pubmed: 26579497
Bioinformatics. 2010 Jun 15;26(12):i158-67
pubmed: 20529901
Mol Genet Genomics. 2018 Oct;293(5):1159-1167
pubmed: 29752546
PLoS Comput Biol. 2017 Oct 16;13(10):e1005781
pubmed: 29036190
BMC Bioinformatics. 2009 Feb 03;10:48
pubmed: 19192299
Cancer Cell. 2010 May 18;17(5):510-22
pubmed: 20399149
Database (Oxford). 2011 Sep 19;2011:bar026
pubmed: 21930502
Biostatistics. 2018 Jan 1;19(1):71-86
pubmed: 28541380
PLoS Comput Biol. 2015 May 07;11(5):e1004071
pubmed: 25950620
Nucleic Acids Res. 2018 Nov 16;46(20):10546-10562
pubmed: 30295871
J Clin Oncol. 2009 Mar 10;27(8):1160-7
pubmed: 19204204
Mol Syst Biol. 2019 Aug;15(8):e8754
pubmed: 31464374
Mol Cancer Res. 2012 Mar;10(3):469-82
pubmed: 22232518
Bioinformatics. 2019 Dec 1;35(23):4886-4897
pubmed: 31077301
Front Genet. 2017 Jun 16;8:84
pubmed: 28670325
Bioinformatics. 2013 Oct 15;29(20):2610-6
pubmed: 23990412