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@InCollection{esposito_vinzi_bridge_2010,
address={Berlin, Heidelberg},
title={A {{Bridge Between PLS Path Modeling}} and {{Multi}}-{{Block Data Analysis}}},
isbn={978-3-540-32825-4 978-3-540-32827-8},
abstract={A situation where J blocks of variables X1, ..., XJ are observed on the same set of individuals is considered in this paper. A factor analysis approach is applied to blocks instead of variables. The latent variables (LV's) of each block should well explain their own block and at the same time the latent variables of same order should be as highly correlated as possible (positively or in absolute value). Two path models can be used in order to obtain the first order latent variables. The first one is related to confirmatory factor analysis: each LV related to one block is connected to all the LV's related to the other blocks. Then, PLS path modeling is used with mode A and centroid scheme. Use of mode B with centroid and factorial schemes is also discussed. The second model is related to hierarchical factor analysis. A causal model is built by relating the LV's of each block Xj to the LV of the super-block XJ+1 obtained by concatenation of X1, ..., XJ . Using PLS estimation of this model with mode A and pathweighting scheme gives an adequate solution for finding the first order latent variables. The use of mode B with centroid and factorial schemes is also discussed. The higher order latent variables are found by using the same algorithms on the deflated blocks. The first approach is compared with the MAXDIFF/MAXBET Van de Geer's algorithm (1984) and the second one with the ACOM algorithm (Chessel \& Hanafi, 1996). Sensory data describing Loire wines are used to illustrate these methods.},
language={en},
booktitle={Handbook of {{Partial Least Squares}}},
publisher={{Springer Berlin Heidelberg}},
author={Michel Tenenhaus and Mohamed Hanafi},
editor={Vincenzo {Esposito Vinzi} and Wynne W. Chin and J{\"o}rg Henseler and Huiwen Wang},
year={2010},
pages={99-123},
file={/home/lrevilla/Zotero/storage/QFWWKF3T/Tenenhaus and Hanafi - 2010 - A Bridge Between PLS Path Modeling and Multi-Block.pdf;/home/lrevilla/Zotero/storage/SJSBHUMA/Tenenhaus and Hanafi - 2010 - A Bridge Between PLS Path Modeling and Multi-Block.pdf},
DOI={10.1007/978-3-540-32827-8_5},
}
@Article{tenenhaus_kernel_2015,
title={Kernel {{Generalized Canonical Correlation Analysis}}},
volume={90},
issn={01679473},
DOI={10.1016/j.csda.2015.04.004},
abstract={A classical problem in statistics is to study relationships between several blocks of variables. The goal is to find variables of one block directly related to variables of other blocks. The Regularized Generalized Canonical Correlation Analysis (RGCCA) is a very attractive framework to study such a kind of relationships between blocks. However, RGCCA captures linear relations between blocks and to assess nonlinear relations we propose a kernel extension of RGCCA.},
language={en},
journal={Computational Statistics \& Data Analysis},
author={Arthur Tenenhaus and Cathy Philippe and Vincent Frouin},
month={oct},
year={2015},
pages={114-131},
file={/home/lrevilla/Zotero/storage/5Y258Q39/Tenenhaus et al. - 2015 - Kernel Generalized Canonical Correlation Analysis.pdf},
}
@Article{tenenhaus_regularized_2011,
title={Regularized {{Generalized Canonical Correlation Analysis}}},
volume={76},
issn={00333123},
DOI={10.1007/s11336-011-9206-8},
abstract={Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.},
number={2},
journal={Psychometrika},
author={Arthur Tenenhaus and Michel Tenenhaus},
month={apr},
year={2011},
keywords={generalized canonical correlation analysis,multi-block data analysis,PLS path modeling,regularized canonical correlation analysis},
pages={257--284},
file={/home/lrevilla/Zotero/storage/9D74WVNF/Tenenhaus, Tenenhaus - 2011 - Regularized Generalized Canonical Correlation Analysis.pdf},
}
@Article{tenenhaus_regularized_2014,
title={Regularized Generalized Canonical Correlation Analysis for Multiblock or Multigroup Data Analysis},
volume={238},
issn={0377-2217},
DOI={10.1016/j.ejor.2014.01.008},
abstract={This paper presents an overview of methods for the analysis of data structured in blocks of variables or in groups of individuals. More specifically, regularized generalized canonical correlation analysis (RGCCA), which is a unifying approach for multiblock data analysis, is extended to be also a unifying tool for multigroup data analysis. The versatility and usefulness of our approach is illustrated on two real datasets.},
number={2},
journal={European Journal of Operational Research},
author={Arthur Tenenhaus and Michel Tenenhaus},
month={oct},
year={2014},
keywords={Multiblock data analysis,Multigroup data analysis,Regularized generalized canonical correlation analysis},
pages={391-403},
file={/home/lrevilla/Zotero/storage/BC6QYM45/Tenenhaus and Tenenhaus - 2014 - Regularized generalized canonical correlation anal.pdf;/home/lrevilla/Zotero/storage/U2QRT5KP/Tenenhaus and Tenenhaus - 2014 - Regularized generalized canonical correlation anal.pdf;/home/lrevilla/Zotero/storage/CMBTPAAX/S0377221714000101.html;/home/lrevilla/Zotero/storage/IQLCGFP4/S0377221714000101.html},
}
@Article{tenenhaus_component-based_2008,
title={Component-Based {{Structural Equation Modelling}}},
volume={19},
issn={1478-3363, 1478-3371},
DOI={10.1080/14783360802159543},
abstract={Two complementary schools have come to the fore in the field of Structural Equation Modelling (SEM): covariance-based SEM and component-based SEM. The first approach has been developed around Karl J\"oreskog and the second one around Herman Wold under the name {"}PLS{"} (Partial Least Squares). Hwang and Takane have proposed a new component-based SEM method named Generalized Structured Component Analysis. Covariance-based SEM is usually used with an objective of model validation and needs a large sample. Component-based SEM is mainly used for score computation and can be carried out on very small samples. In this research, we will explore the use of ULS-SEM, PLS, GSCA, path analysis on block principal components and path analysis on block scales on customer satisfaction data. Our conclusion is that score computation and bootstrap validation are very insensitive to the choice of the method when the blocks are homogenous.},
language={en},
number={7-8},
journal={Total Quality Management \& Business Excellence},
author={Michel Tenenhaus},
month={aug},
year={2008},
pages={871-886},
file={/home/lrevilla/Zotero/storage/JF2W63T6/Tenenhaus - 2008 - Component-based Structural Equation Modelling.pdf},
}
@Article{tenenhaus_variable_2014,
title={Variable Selection for Generalized Canonical Correlation Analysis},
volume={15},
issn={1465-4644},
DOI={10.1093/biostatistics/kxu001},
abstract={Abstract. Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to 3 or more sets of},
language={en},
number={3},
journal={Biostatistics},
author={Arthur Tenenhaus and Cathy Philippe and Vincent Guillemot and Kim-Anh {Le Cao} and Jacques Grill and Vincent Frouin},
month={jul},
year={2014},
pages={569-583},
file={/home/lrevilla/Zotero/storage/5MDM2AP3/Tenenhaus et al. - 2014 - Variable selection for generalized canonical corre.pdf;/home/lrevilla/Zotero/storage/5ZUE8ASZ/Tenenhaus et al. - 2014 - Variable selection for generalized canonical corre.pdf;/home/lrevilla/Zotero/storage/9RYKL5UV/Tenenhaus et al. - 2014 - Variable selection for generalized canonical correlation analysis(2).pdf;/home/lrevilla/Zotero/storage/6D3W6W98/224062.html;/home/lrevilla/Zotero/storage/Q6I89C9U/224062.html},
}
@Article{argelaguet_multi-omics_2018,
title={Multi-{{Omics Factor Analysis}}\textemdash{}a Framework for Unsupervised Integration of Multi-Omics Data Sets},
author={Ricard Argelaguet and Britta Velten and Damien Arnol and Sascha Dietrich and Thorsten Zenz and John C Marioni and Florian Buettner and Wolfgang Huber and Oliver Stegle},
year={2018},
month={jun},
volume={14},
pages={e8124},
issn={1744-4292},
DOI={10.15252/msb.20178124},
file={/home/lrevilla/Zotero/storage/N28USY7S/Argelaguet et al. - 2018 - Multi-Omics Factor Analysis—a framework for unsupe.pdf;/home/lrevilla/Zotero/storage/UDUQS6CN/msb.html},
journal={Molecular Systems Biology},
keywords={data integration,dimensionality reduction,multi-omics,personalized medicine,single-cell omics},
number={6},
}
@Article{culhane_cross-platform_2003,
title={Cross-Platform Comparison and Visualisation of Gene Expression Data Using Co-Inertia Analysis},
author={Aed\'in C. Culhane and Guy Perri{\a`e}re and Desmond G. Higgins},
year={2003},
month={dec},
volume={4},
pages={59},
issn={1471-2105},
DOI={10.1186/1471-2105-4-59},
copyright={2003 Culhane et al; licensee BioMed Central Ltd.},
file={/home/lrevilla/Zotero/storage/2XBBTNMP/Culhane et al. - 2003 - Cross-platform comparison and visualisation of gen.pdf;/home/lrevilla/Zotero/storage/6A3VA7MG/1471-2105-4-59.html},
journal={BMC Bioinformatics},
language={En},
number={1},
}
@Article{vito_multi-study_2019,
title={Multi-Study Factor Analysis},
author={Roberta De Vito and Ruggero Bellio and Lorenzo Trippa and Giovanni Parmigiani},
year={2019},
volume={75},
pages={337--346},
issn={1541-0420},
DOI={10.1111/biom.12974},
copyright={\textcopyright{} 2018 Wiley Periodicals, Inc.},
file={/home/lrevilla/Zotero/storage/JAKB848N/Vito et al. - 2019 - Multi-study factor analysis.pdf;/home/lrevilla/Zotero/storage/4KE3NB2L/biom.html},
journal={Biometrics},
keywords={cross-study analysis,Dimension reduction,ECM algorithm,gene expression,meta-analysis,reproducibility},
language={en},
number={1},
}
@Article{sherryConductingInterpretingCanonical1981,
title={Conducting and {{Interpreting Canonical Correlation Analysis}} in {{Personality Research}}: {{A User}}-{{Friendly Primer}}},
author={Alissa Sherry and Robin K Henson},
year={1981},
abstract={The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely under-stood Pearson r correlation coefficient. An understanding of CCA can lead to a more global ap-preciation of other univariate and multivariate methods in the GLM. We attempt to demonstrate CCA with basic language, using technical terminology only when necessary for understanding and use of the method. We present an entire example of a CCA analysis using SPSS (Version 11.0) with personality data. Many applied behavioral researchers are not aware that there is a general linear model (GLM) that governs most classical univariate (e.g., analysis of variance [ANOVA], regression) and multivariate (e.g., multivariate ANOVA [MANOVA], descriptive discriminant analysis) statistical methods. Ac-cordingly, many persons view these statistical methods as separate entities rather than conceptualizing their distinct similarities within the GLM. For example, because all classi-cal parametric analyses are part of the GLM, all of these anal-yses have certain things in common, including the facts that they (a) are ultimately correlational in nature, (b) yield r 2 -type effect sizes, (c) maximize shared variance between variables or between sets of variables, and (d) apply weights to observed variables to create synthetic (i.e., unobserved, la-tent) variables that often become the focus of the analysis (cf. Knowledge of the commonalities among statistical analy-ses is in stark contrast to the often compartmentalized statis-tical education that many graduate students and faculty have received. Unfortunately, this compartmentalization can lead to rigidity of thought concerning the methods as opposed to a fluid understanding of their purpose and utility, thereby hin-dering appropriate methodological applications in applied psychological research. Indeed, at least partially because of this educational paradigm, it not uncommon to see some graduate students physically shudder at the thought of endur-ing advanced methodological coursework. It should not be surprising, then, to find some graduate students taking great lengths to desperately avoid methodology curricula and, in extreme cases, seeking psychotherapy to reduce the systemic anxiety these courses seem to invoke! Statistics anxiety notwithstanding, the GLM provides a framework for understanding all classical analyses in terms of the simple Pearson r correlation coefficient. We demon-strate later, for example, the interpretation of a canonical cor-relation analysis (CCA), which has as its foundation the Pearson r correlation. The GLM can also be conceptualized as a hierarchal family, with CCA serving as the parent analy-sis. Contrary to the compartmentalized understanding of sta-tistical methods held by many researchers, CCA subsumes both univariate and multivariate methods as special cases},
file={/home/lluis/Zotero/storage/AVERUJQY/Sherry, Henson - 1981 - Conducting and Interpreting Canonical Correlation Analysis in Personality Research A User-Friendly Primer(2).pdf},
}
@Article{hasler_uncoupling_2016,
title={Uncoupling of Mucosal Gene Regulation, {{mRNA}} Splicing and Adherent Microbiota Signatures in Inflammatory Bowel Disease},
author={Robert H{\"a}sler and Raheleh Sheibani-Tezerji and Anupam Sinha and Matthias Barann and Ateequr Rehman and Daniela Esser and Konrad Aden and Carolin Knecht and Berenice Brandt and Susanna Nikolaus and Sascha Sch{\"a}uble and Christoph Kaleta and Andre Franke and Christoph Fretter and Werner M{\"u}ller and Marc-Thorsten Thorsten H{\"u}tt and Michael Krawczak and Stefan Schreiber and Philip Rosenstiel},
volume={66},
number={12},
pages={2087--2097},
year={2017},
issn={0017-5749},
DOI={10.1136/gutjnl-2016-311651},
publisher={BMJ Publishing Group},
abstract={Objective An inadequate host response to the intestinal microbiota likely contributes to the manifestation and progression of human inflammatory bowel disease (IBD). However, molecular approaches to unravelling the nature of the defective crosstalk and its consequences for intestinal metabolic and immunological networks are lacking. We assessed the mucosal transcript levels, splicing architecture and mucosa-attached microbial communities of patients with IBD to obtain a comprehensive view of the underlying, hitherto poorly characterised interactions, and how these are altered in IBD.Design Mucosal biopsies from Crohn{\textquoteright}s disease and patients with UC, disease controls and healthy individuals (n=63) were subjected to microbiome, transcriptome and splicing analysis, employing next-generation sequencing. The three data levels were integrated by different bioinformatic approaches, including systems biology-inspired network and pathway analysis.Results Microbiota, host transcript levels and host splicing patterns were influenced most strongly by tissue differences, followed by the effect of inflammation. Both factors point towards a substantial disease-related alteration of metabolic processes. We also observed a strong enrichment of splicing events in inflamed tissues, accompanied by an alteration of the mucosa-attached bacterial taxa. Finally, we noted a striking uncoupling of the three molecular entities when moving from healthy individuals via disease controls to patients with IBD.Conclusions Our results provide strong evidence that the interplay between microbiome and host transcriptome, which normally characterises a state of intestinal homeostasis, is drastically perturbed in Crohn{\textquoteright}s disease and UC. Consequently, integrating multiple OMICs levels appears to be a promising approach to further disentangle the complexity of IBD.},
file={/home/lrevilla/Zotero/storage/AHYPXCPK/Häsler et al. - 2017 - Uncoupling of mucosal gene regulation, mRNA splici.pdf},
eprint={https://gut.bmj.com/content/66/12/2087.full.pdf},
journal={Gut},
pmid={27694142},
}
@Article{koh_iomicspass_2019,
title={{{iOmicsPASS}}: Network-Based Integration of Multiomics Data for Predictive Subnetwork Discovery},
shorttitle={{{iOmicsPASS}}},
author={Hiromi W. L. Koh and Damian Fermin and Christine Vogel and Kwok Pui Choi and Rob M. Ewing and Hyungwon Choi},
year={2019},
month={jul},
volume={5},
pages={1--10},
publisher={{Nature Publishing Group}},
issn={2056-7189},
DOI={10.1038/s41540-019-0099-y},
copyright={2019 The Author(s)},
file={/home/lrevilla/Zotero/storage/HSD3I87Q/Koh et al. - 2019 - iOmicsPASS network-based integration of multiomic.pdf;/home/lrevilla/Zotero/storage/5MJDW5J6/s41540-019-0099-y.html},
journal={npj Systems Biology and Applications},
language={en},
number={1},
}
@Article{chung_multi-omics_2019,
title={A Multi-Omics Data Simulator for Complex Disease Studies and Its Application to Evaluate Multi-Omics Data Analysis Methods for Disease Classification},
author={Ren-Hua Chung and Chen-Yu Kang},
year={2019},
month={may},
volume={8},
issn={2047-217X},
DOI={10.1093/gigascience/giz045},
file={/home/lrevilla/Zotero/storage/Y4F92I8F/Chung and Kang - 2019 - A multi-omics data simulator for complex disease s.pdf},
journal={GigaScience},
keywords={complex disease study,multi-omics data,simulation tool},
language={eng},
number={5},
pmcid={PMC6486474},
pmid={31029063},
}
@Article{wu_selective_2019,
title={A {{Selective Review}} of {{Multi}}-{{Level Omics Data Integration Using Variable Selection}}},
author={Cen Wu and Fei Zhou and Jie Ren and Xiaoxi Li and Yu Jiang and Shuangge Ma},
year={2019},
month={mar},
volume={8},
pages={4},
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Subject{\_}term: Computational biology and bioinformatics;Data integration
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