MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination

Paries Martin (), Vigneau Evelyne, Huneau Adeline, Lantz Olivier and Bougeard Stéphanie ()
Additional contact information
Paries Martin: Oniris, INRAE, StatSC, 44300 Nantes, France
Vigneau Evelyne: Oniris, INRAE, StatSC, 44300 Nantes, France
Huneau Adeline: Anses, Epidemiology, Health and Welfare, Laboratory of Ploufragan-Plouzané-Niort, Ploufragan, France
Lantz Olivier: Clinical Immunology Laboratory, Institute Curie, Paris, France
Bougeard Stéphanie: Anses, Epidemiology, Health and Welfare, Laboratory of Ploufragan-Plouzané-Niort, Ploufragan, France

The International Journal of Biostatistics, 2024, vol. 20, issue 2, 389-406

Abstract: Studying a large number of variables measured on the same observations and organized in blocks – denoted multiblock data – is becoming standard in several domains especially in biology. To explore the relationships between all these variables – at the block- and the variable-level – several exploratory multiblock methods were proposed. However, most of them are only designed for numeric variables. In reality, some data sets contain variables of different measurement levels (i.e., numeric, nominal, ordinal). In this article, we focus on exploratory multiblock methods that handle variables at their appropriate measurement level. Multi-Block Principal Component Analysis with Optimal Scaling (MBPCA-OS) is proposed and applied to multiblock data from the CURIE-O-SA French cohort. In this study, variables are of different measurement levels and organized in four blocks. The objective is to study the immune responses according to the SARS-CoV-2 infection and vaccination statuses, the symptoms and the participant’s characteristics.

Keywords: multiblock analysis; exploratory analysis; optimal scaling; level of scaling; categorical variables; SARS-CoV-2 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2023-0062 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:20:y:2024:i:2:p:389-406:n:1014

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2023-0062

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().