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A variant of sparse partial least squares for variable selection and data exploration

Megan J. Olson Hunt, Lisa Weissfeld, Robert M. Boudreau, Howard Aizenstein, Anne B. Newman, Eleanor M. Simonsick, Dane R. Van Domelen, Fridtjof Thomas, Kristine Yaffe, Caterina Rosano
2014 Frontiers in Neuroinformatics  
When data are sparse and/or predictors multicollinear, current implementation of sparse partial least squares (SPLS) does not give estimates for non-selected predictors nor provide a measure of inference  ...  As a result, all-possible SPLS gives more information than the dichotomous output of traditional SPLS, making it useful when undertaking data exploration and hypothesis generation for a large number of  ...  A full list of principal CHS investigators and institutions can be found at  ... 
doi:10.3389/fninf.2014.00018 pmid:24624079 pmcid:PMC3939647 fatcat:zhoza6nje5hmrixfmvfbzlf4vm

Group-wise partial least square regression

José Camacho, Edoardo Saccenti
2017 Journal of Chemometrics  
ACKNOWLEDGEMENTS Dr Ewa Szymańska is gratefully acknowledged for making available the MATLAB implementation of the SPLS algorithm.  ...  This work is partly supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through project TIN2014-60346-R and by the EU Commission through the FP7 project INFECT (Contract No  ...  The regression coefficients for the PLS, SPLS, and GPLS models are given FIGURE 9 Regression coefficients ( ) for the first 3 latent variables for partial least squares (PLS), sparse partial least squares  ... 
doi:10.1002/cem.2964 fatcat:pfj6gunpxzdxncytcnstckfxm4

Penalized partial least squares for pleiotropy

Camilo Broc, Therese Truong, Benoit Liquet
2021 BMC Bioinformatics  
The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets.  ...  Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables.  ...  Acknowledgements The authors acknowledge Pascal Guénel for providing the breast and thyroid cancer data.  ... 
doi:10.1186/s12859-021-03968-1 pmid:33627076 fatcat:yjviawoeu5fsfosshtbyu4oyty

A multivariate approach to investigate the combined biological effects of multiple exposures

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
2018 Journal of Epidemiology and Community Health  
PLS algorithms can easily scale to highdimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological  ...  To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful  ...  Results are presented for the sparse PLS models performing variable selection of both exposures and proteins. PLS, partial least squares.  ... 
doi:10.1136/jech-2017-210061 pmid:29563153 pmcid:PMC6031275 fatcat:gi26ayb23vfydgflezyny7tqku

Sparse reduced-rank regression for integrating omics data

Haileab Hilafu, Sandra E. Safo, Lillian Haine
2020 BMC Bioinformatics  
It also assumes variables for each data type are independent, and thus ignores correlations that exist between variables both within each data type and across the data types.  ...  A popular epidemiology approach is to consider an association of each of the predictors with each of the response using a univariate linear regression model, and to select predictors that meet a priori  ...  Acknowledgments We are grateful to the Emory Predictive Health Institute for providing us with the genomics, metabolomics, and clinical data used in the real data analysis.  ... 
doi:10.1186/s12859-020-03606-2 pmid:32620072 fatcat:jzlzt4ng2zc2znjnscjldubv24

Semi-supervised Multivariate Statistical Network Monitoring for Learning Security Threats

Jose Camacho, Gabriel Macia-Fernandez, Noemi Marta Fuentes-Garcia, Edoardo Saccenti
2019 IEEE Transactions on Information Forensics and Security  
The supervised learning is based on an extension of the gradient descent method based on Partial Least Squares (PLS). Moreover, we enhance this method by using sparse PLS variants.  ...  This paper presents a semi-supervised approach for intrusion detection.  ...  ACKNOWLEDGMENT This work is partly supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through projects TIN2014-60346-R and TIN2017-83494-R.  ... 
doi:10.1109/tifs.2019.2894358 fatcat:2a7oxvm6d5chvkxqkduvqahife

Temporal prediction of future state occupation in a multistate model from high-dimensional baseline covariates via pseudo-value regression

Sandipan Dutta, Susmita Datta, Somnath Datta
2016 Figshare  
least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants.  ...  With the advent of high-throughput genomic and proteomic assays, a clinician may intent to use such high-dimensional covariates in making better prediction of state occupation.  ...  Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society, Series B , 3-25.  ... 
doi:10.6084/m9.figshare.4487504 fatcat:4lfpmcdhrrgztlofepv4occxgq

Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression

Omid Kohannim, Derrek P. Hibar, Neda Jahanshad, Jason L. Stein, Xue Hua, Arthur W. Toga, Clifford R. Jack, Michael W. Weinen, Paul M. Thompson
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
Penalized or sparse regression methods are gaining increasing attention in imaging genomics, as they can select optimal regressors from a large set of predictors whose individual effects are small or mostly  ...  The rs9933137 variant in the RBFOX1 gene was a highly contributory genotype, along with rs10845840 in GRIN2B and rs2456930, discovered previously in a univariate genomewide search.  ...  Acknowledgments ADNI data collection was supported by federal and private funds including NIH grants U01 AG024904, P30 AG010129, K01 AG030514, and the Dana Foundation.  ... 
doi:10.1109/isbi.2012.6235766 pmid:22903144 pmcid:PMC3420969 dblp:conf/isbi/KohannimHJSHTJWT12 fatcat:rbmdrvlwrrctvbpobryuvqnvb4

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

Rosalba Calvini, Alessandro Ulrici, Jose Manuel Amigo
2015 Chemometrics and Intelligent Laboratory Systems  
In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification  ...  The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero.  ...  ACKNOWLEDGMENTS The authors wish to thank Luigi Bellucci (Caffè Molinari spa) for providing the coffee samples and technical support.  ... 
doi:10.1016/j.chemolab.2015.07.010 fatcat:dgtpqo7qrrg7rly5uvyfhnokgy

Regularized estimation of large-scale gene association networks using graphical Gaussian models

Nicole Krämer, Juliane Schäfer, Anne-Laure Boulesteix
2009 BMC Bioinformatics  
A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations.  ...  Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data.  ...  We thank Lukas Meier and Mikio L. Braun for constructive comments on model selection, and Animesh Acharjee for helpful feedback on the R package "parcor".  ... 
doi:10.1186/1471-2105-10-384 pmid:19930695 pmcid:PMC2808166 fatcat:klorifcsgjezzcywgtfqq6vrpm

Gene-Environment Interaction: A Variable Selection Perspective [article]

Fei Zhou, Jie Ren, Xi Lu, Shuangge Ma, Cen Wu
2020 arXiv   pre-print
Then, after a brief introduction on the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms respectively under a variety of conceptual  ...  Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G×E studies have also been provided.  ...  Acknowledgement We thank the editor and reviewers for their invitation, careful review and insightful comments, leading to a significant improvement of this article.  ... 
arXiv:2003.02930v1 fatcat:gha2quplljaxnprpv4cfnfmqgi

A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data

Panpan Wang, Mohammad Rahman, Li Jin, Momiao Xiong
2016 BMC Genomics  
To overcome limitations of the traditional genetic pleiotropic analysis of multiple phenotypes, we develop sparse structural equation models (SEMs) as a general framework for a new paradigm of genetic  ...  The widely used genetic pleiotropic analyses of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes.  ...  Availability of data and materials Exome sequence data were generated from the NHLBI's Exome Sequencing Project (ESP) and have been deposited in dbGaP as part of the ESP cohort data.  ... 
doi:10.1186/s12864-016-3169-1 pmid:27821073 pmcid:PMC5100198 fatcat:yjq5oafcnbeljbzhbdc7qxjgca

Recent trends in multi-block data analysis in chemometrics for multi-source data integration

Puneet Mishra, Jean Michel Roger, Delphine Jouan-Rimbaud-Bouveresse, Alessandra Biancolillo, Federico Marini, Alison Nordon, Douglas N. Rutledge
2021 TrAC. Trends in analytical chemistry  
Sometimes classical chemometric methods such as principal component analysis (PCA) and partial least squares regression (PLS) are not adequate to analyze this kind of data.  ...  Finally, a summary of software resources available for multi-block data analysis is provided.  ...  order data Sequential preprocessing through orthogonalization (SPORT) Based on sequential orthogonalized partial least-squares regression Includes a combination of partial least-squares regression and  ... 
doi:10.1016/j.trac.2021.116206 fatcat:chqzfyrsuzay7cvdw3haybkvhy

Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

Justo Lorenzo Bermejo
2016 BMC Genetics  
Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference.  ...  Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data.  ...  Justo Lorenzo Bermejo thanks all members of the GAW19 working group Population-Based Association for their participation in the workshop and for the fruitful discussions.  ... 
doi:10.1186/s12863-015-0310-0 pmid:26866664 pmcid:PMC4895250 fatcat:wyl4savbgjbevhlraqgfs6eu34

The group exponential lasso for bi-level variable selection

Patrick Breheny
2015 Biometrics  
An ideal penalized regression approach would select variables by balancing both the direct evidence of a feature's importance as well as the indirect evidence offered by the grouping structure.  ...  Finally, we apply these methods to the problem of detecting rare variants in a genetic association study.  ...  A similar result holds, at least for τ = 0.3 and τ = 0.5, with respect to variable selection.  ... 
doi:10.1111/biom.12300 pmid:25773593 fatcat:x66acnwxvzd2jdx2gtjqfzbp3y
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