1
|
A new active zero set descent algorithm for least absolute deviation with generalized LASSO penalty. J Korean Stat Soc 2022. [DOI: 10.1007/s42952-022-00192-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
2
|
Otto P, Steinert R. Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2107530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
| | - Rick Steinert
- European University Viadrina, Frankfurt (Oder), Germany
| |
Collapse
|
3
|
Chardin D, Humbert O, Bailleux C, Burel-Vandenbos F, Rigau V, Pourcher T, Barlaud M. Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies. BMC Bioinformatics 2021; 22:594. [PMID: 34911437 PMCID: PMC8672607 DOI: 10.1186/s12859-021-04478-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 10/29/2021] [Indexed: 11/25/2022] Open
Abstract
Background Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH. Results PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate. Conclusion PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04478-w.
Collapse
Affiliation(s)
- David Chardin
- Transporters in imaging and Radiotherapy in Oncology (TIRO), Direction de la Recherche Fondamentale (DRF), Institute des sciences du vivant Fréderic Joliot, Commissariat à l'Energie Atomique et aux énergies alternatives (CEA), Université Côte d'Azur (UCA), Nice, France.,Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur (UCA), Nice, France
| | - Olivier Humbert
- Transporters in imaging and Radiotherapy in Oncology (TIRO), Direction de la Recherche Fondamentale (DRF), Institute des sciences du vivant Fréderic Joliot, Commissariat à l'Energie Atomique et aux énergies alternatives (CEA), Université Côte d'Azur (UCA), Nice, France.,Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur (UCA), Nice, France
| | - Caroline Bailleux
- Transporters in imaging and Radiotherapy in Oncology (TIRO), Direction de la Recherche Fondamentale (DRF), Institute des sciences du vivant Fréderic Joliot, Commissariat à l'Energie Atomique et aux énergies alternatives (CEA), Université Côte d'Azur (UCA), Nice, France.,Department of Oncology, Centre Antoine Lacassagne, Université Côte d'Azur (UCA), Nice, France
| | - Fanny Burel-Vandenbos
- Central Laboratory of Pathology, University Hospital and Institute of Biology Valrose, Inserm U1091 - CNRS UMR7277, University Côte d'Azur, Nice, France
| | - Valerie Rigau
- Department of Pathology and Oncobiology, University Hospital, Montpellier, France.,Institute for Neurosciences of Montpellier, INSERM U1051, Montpellier, France
| | - Thierry Pourcher
- Transporters in imaging and Radiotherapy in Oncology (TIRO), Direction de la Recherche Fondamentale (DRF), Institute des sciences du vivant Fréderic Joliot, Commissariat à l'Energie Atomique et aux énergies alternatives (CEA), Université Côte d'Azur (UCA), Nice, France
| | - Michel Barlaud
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis (I3S), Université Côte d'Azur (UCA), Centre de Recherche Scientifique (CNRS), Sophia Antipolis, France.
| |
Collapse
|
5
|
Ewald K, Schneider U. On the distribution, model selection properties and uniqueness of the Lasso estimator in low and high dimensions. Electron J Stat 2020. [DOI: 10.1214/20-ejs1687] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|