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Scientific article
English

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

Contributors

Codesido Sanchez, Santiago; Hanafi, Mohamed; Gagnebin, Yoric; Gonzalez Ruiz, Victor; Rudaz, Serge; Boccard, Julien

Published in

Bioinformatics

Publication date

2020

Abstract

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.

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

CODESIDO SANCHEZ, Santiago et al. Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets. In: Bioinformatics, 2020. doi: 10.1093/bioinformatics/btaa954

Main files (1)

Article (Published version)

Identifiers

Journal ISSN

1367-4803
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Technical informations

Creation

26/01/2021 16:05:00

First validation

26/01/2021 16:05:00

Update

16/03/2023 00:09:43

Status update

16/03/2023 00:09:43

Last indexation

18/03/2025 09:25:10
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