Benoît Liquet1, Pierre Lafaye de Micheaux2, Boris P Hejblum3, Rodolphe Thiébaut3. 1. School of Mathematics and Physics, The University of Queensland, Brisbane 4066, Australia, ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, Brisbane, Australia. 2. CREST, ENSAI, Campus de Ker-Lann, Rue Blaise Pascal, BP 37203, 35172 Bruz cedex, France. 3. Inria, SISTM, Talence and Inserm, U897, Bordeaux, Bordeaux University, Bordeaux and Vaccine Research Institute, Creteil, France.
Abstract
MOTIVATION: The association between two blocks of 'omics' data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. RESULTS: We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine. AVAILABILITY AND IMPLEMENTATION: The approach is implemented in a comprehensive R package called sgPLS available on the CRAN. CONTACT: b.liquet@uq.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The association between two blocks of 'omics' data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. RESULTS: We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine. AVAILABILITY AND IMPLEMENTATION: The approach is implemented in a comprehensive R package called sgPLS available on the CRAN. CONTACT: b.liquet@uq.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Savanah Senn; Sharmodeep Bhattacharyya; Gerald Presley; Anne E Taylor; Bruce Nash; Ray A Enke; Karen B Barnard-Kubow; Jillian Ford; Brandon Jasinski; Yekaterina Badalova Journal: Microorganisms Date: 2022-06-14
Authors: Pooja Jain; Paolo Vineis; Benoît Liquet; Jelle Vlaanderen; Barbara Bodinier; Karin van Veldhoven; Manolis Kogevinas; Toby J Athersuch; Laia Font-Ribera; Cristina M Villanueva; Roel Vermeulen; Marc Chadeau-Hyam Journal: J Epidemiol Community Health Date: 2018-03-21 Impact factor: 3.710
Authors: Roel Vermeulen; Fatemeh Saberi Hosnijeh; Barbara Bodinier; Lützen Portengen; Benoît Liquet; Javiera Garrido-Manriquez; Henk Lokhorst; Ingvar A Bergdahl; Soterios A Kyrtopoulos; Ann-Sofie Johansson; Panagiotis Georgiadis; Beatrice Melin; Domenico Palli; Vittorio Krogh; Salvatore Panico; Carlotta Sacerdote; Rosario Tumino; Paolo Vineis; Raphaële Castagné; Marc Chadeau-Hyam; Maria Botsivali; Aristotelis Chatziioannou; Ioannis Valavanis; Jos C S Kleinjans; Theo M C M de Kok; Hector C Keun; Toby J Athersuch; Rachel Kelly; Per Lenner; Goran Hallmans; Euripides G Stephanou; Antonis Myridakis; Manolis Kogevinas; Lucia Fazzo; Marco De Santis; Pietro Comba; Benedetta Bendinelli; Hannu Kiviranta; Panu Rantakokko; Riikka Airaksinen; Paivi Ruokojarvi; Mark Gilthorpe; Sarah Fleming; Thomas Fleming; Yu-Kang Tu; Thomas Lundh; Kuo-Liong Chien; Wei J Chen; Wen-Chung Lee; Chuhsing Kate Hsiao; Po-Hsiu Kuo; Hung Hung; Shu-Fen Liao Journal: Int J Cancer Date: 2018-04-26 Impact factor: 7.396
Authors: Kiyoshi F Fukutani; Cristiana M Nascimento-Carvalho; Maiara L Bouzas; Juliana R Oliveira; Aldina Barral; Tim Dierckx; Ricardo Khouri; Helder I Nakaya; Bruno B Andrade; Johan Van Weyenbergh; Camila I de Oliveira Journal: Front Microbiol Date: 2018-11-09 Impact factor: 5.640
Authors: Rodolphe Thiébaut; Boris P Hejblum; Hakim Hocini; Henri Bonnabau; Jason Skinner; Monica Montes; Christine Lacabaratz; Laura Richert; Karolina Palucka; Jacques Banchereau; Yves Lévy Journal: Front Immunol Date: 2019-04-24 Impact factor: 7.561
Authors: Anne Rechtien; Laura Richert; Hadrien Lorenzo; Gloria Martrus; Boris Hejblum; Christine Dahlke; Rahel Kasonta; Madeleine Zinser; Hans Stubbe; Urte Matschl; Ansgar Lohse; Verena Krähling; Markus Eickmann; Stephan Becker; Rodolphe Thiébaut; Marcus Altfeld; Marylyn M Addo Journal: Cell Rep Date: 2017-08-29 Impact factor: 9.423