Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets

Article Article scientifique Article Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets eng 2020 author Codesido Sanchez Santiago 812906 author Hanafi Mohamed 884769 author Gagnebin Yoric 677326 author Gonzalez Ruiz Victor 831974 author Rudaz Serge 0000-0002-4180-5417 28978 author Boccard Julien 0000-0001-5913-9566 507373 10.1093/bioinformatics/btaa954 33165510 1367-4803 Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed. Dewey 615 31 Section des sciences pharmaceutiques 17 false 881 Sciences analytiques Bioinformatics 946bf4eb-b3fb-477c-ba93-f673a0d331e6 Article (Published version) Restricted 313.pdf 2362689 application/pdf restrictedAccess Faculté des sciences / Section des sciences pharmaceutiques

Title	Published in	Access level	OA Policy	Year	Views	Downloads

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets	Bioinformatics			2020	274	0

Archive ouverte UNIGE

Hanafi, Mohamed