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A Sparse PLS for Variable Selection when Integrating Omics Data
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Kim-Anh Lê Cao
Veröffentlicht/Copyright:
18. November 2008
Recent biotechnology advances allow for multiple types of omics data, such as transcriptomic, proteomic or metabolomic data sets to be integrated. The problem of feature selection has been addressed several times in the context of classification, but needs to be handled in a specific manner when integrating data. In this study, we focus on the integration of two-block data that are measured on the same samples. Our goal is to combine integration and simultaneous variable selection of the two data sets in a one-step procedure using a Partial Least Squares regression (PLS) variant to facilitate the biologists' interpretation. A novel computational methodology called ``sparse PLS" is introduced for a predictive analysis to deal with these newly arisen problems. The sparsity of our approach is achieved with a Lasso penalization of the PLS loading vectors when computing the Singular Value Decomposition.Sparse PLS is shown to be effective and biologically meaningful. Comparisons with classical PLS are performed on a simulated data set and on real data sets. On one data set, a thorough biological interpretation of the obtained results is provided. We show that sparse PLS provides a valuable variable selection tool for highly dimensional data sets.
Published Online: 2008-11-18
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent
- Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
- Modeling DNA Methylation in a Population of Cancer Cells
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- The Estimator of the Optimal Measure of Allelic Association: Mean, Variance and Probability Distribution When the Sample Size Tends to Infinity
- Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation
- A Comparison of Normalization Techniques for MicroRNA Microarray Data
- Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power
- Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
- Data Distribution of Short Oligonucleotide Expression Arrays and Its Application to the Construction of a Generalized Intellectual Framework
- Approximately Sufficient Statistics and Bayesian Computation
- A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
- Statistical Methods in Integrative Analysis for Gene Regulatory Modules
- Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information
- Pattern Classification of Phylogeny Signals
- A Unification of Multivariate Methods for Meta-Analysis of Genetic Association Studies
- Importance Sampling for the Infinite Sites Model
- Supervised Distance Matrices
- Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Software Communication
- TRAB: Testing Whether Mutation Frequencies Are Above an Unknown Background
Schlagwörter für diesen Artikel
joint analysis;
two-block data set;
multivariate regression;
dimension reduction
Artikel in diesem Heft
- Article
- Self-Organizing Maps with Statistical Phase Synchronization (SOMPS) for Analyzing Cell Cycle-Specific Gene Expression Data
- Coalescent Time Distributions in Trees of Arbitrary Size
- Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis
- Nonparametric Functional Mapping of Quantitative Trait Loci Underlying Programmed Cell Death
- Accommodating Uncertainty in a Tree Set for Function Estimation
- Drifting Markov Models with Polynomial Drift and Applications to DNA Sequences
- Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
- Calculating Confidence Intervals for Prediction Error in Microarray Classification Using Resampling
- Structure Learning in Nested Effects Models
- Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study
- Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
- Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing
- Re-Cracking the Nucleosome Positioning Code
- Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation
- A SNP Streak Model for the Identification of Genetic Regions Identical-by-descent
- Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
- Modeling DNA Methylation in a Population of Cancer Cells
- Phenotyping Genetic Diseases Using an Extension of µ-Scores for Multivariate Data
- The Estimator of the Optimal Measure of Allelic Association: Mean, Variance and Probability Distribution When the Sample Size Tends to Infinity
- Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation
- A Comparison of Normalization Techniques for MicroRNA Microarray Data
- Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power
- Estimating Number of Clusters Based on a General Similarity Matrix with Application to Microarray Data
- Data Distribution of Short Oligonucleotide Expression Arrays and Its Application to the Construction of a Generalized Intellectual Framework
- Approximately Sufficient Statistics and Bayesian Computation
- A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
- Statistical Methods in Integrative Analysis for Gene Regulatory Modules
- Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information
- Pattern Classification of Phylogeny Signals
- A Unification of Multivariate Methods for Meta-Analysis of Genetic Association Studies
- Importance Sampling for the Infinite Sites Model
- Supervised Distance Matrices
- Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments
- A Sparse PLS for Variable Selection when Integrating Omics Data
- Software Communication
- TRAB: Testing Whether Mutation Frequencies Are Above an Unknown Background
