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Identification of significant genetic variants via SLOPE, and its extension to group SLOPE

Authors:

Alexej Gossmann,

Yu-Ping WangAuthors Info & Claims

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 232 - 240

https://doi.org/10.1145/2808719.2808743

Published: 09 September 2015 Publication History

Abstract

The method of Sorted L-One Penalized Estimation, abbreviated as SLOPE, is a novel sparse regression method for model selection introduced in a sequence of recent papers, [4], [3] and [7] by Bogdan, van den Berg, Sabatti, Su and Candes. It estimates the coefficients of a linear model that possibly has more unknown parameters than observations. In many settings the SLOPE method is shown to successfully control the false discovery rate (the proportion of the irrelevant among all selected predictors) at a user specified level. In this paper we evaluate its performance on genetic data, and show its superiority over LASSO which is a related and popular method. Often in genetic data sets, group structures among the predictor variables are given as prior knowledge, such as SNPs in a gene or genes in a pathway. Following this motivation we extend SLOPE in the spirit of Group LASSO to Group SLOPE, a method that can handle group structures between the predictor variables, which are ubiquitous in real genetic data. Our simulation results show that the proposed Group SLOPE method is capable of controlling the false discovery rate at a specified level. Moreover, our simulations show that compared to Group LASSO, Group SLOPE in general achieves a higher power as well as a lower false discovery rate.

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Cited By

Bassett ROh M(2024)Estimating a signal subspace in the presence of impulsive noiseStatistics and Computing10.1007/s11222-024-10528-z35:1Online publication date: 25-Nov-2024
https://doi.org/10.1007/s11222-024-10528-z
Gossmann ACao SBrzyski DZhao LDeng HWang Y(2018)A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate ControlIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.278010615:4(1066-1078)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TCBB.2017.2780106
Brzyski DGossmann ASu WBogdan M(2018)Group SLOPE – Adaptive Selection of Groups of PredictorsJournal of the American Statistical Association10.1080/01621459.2017.1411269114:525(419-433)Online publication date: 6-Aug-2018
https://doi.org/10.1080/01621459.2017.1411269
Show More Cited By

Index Terms

Identification of significant genetic variants via SLOPE, and its extension to group SLOPE
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

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cover image ACM Conferences

BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

September 2015

683 pages

ISBN:9781450338530

DOI:10.1145/2808719

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 September 2015

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National Institutes of Health

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SIGBio

BCB '15: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

September 9 - 12, 2015

Georgia, Atlanta

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BCB '15 Paper Acceptance Rate 48 of 141 submissions, 34%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Bassett ROh M(2024)Estimating a signal subspace in the presence of impulsive noiseStatistics and Computing10.1007/s11222-024-10528-z35:1Online publication date: 25-Nov-2024
https://doi.org/10.1007/s11222-024-10528-z
Gossmann ACao SBrzyski DZhao LDeng HWang Y(2018)A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate ControlIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.278010615:4(1066-1078)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TCBB.2017.2780106
Brzyski DGossmann ASu WBogdan M(2018)Group SLOPE – Adaptive Selection of Groups of PredictorsJournal of the American Statistical Association10.1080/01621459.2017.1411269114:525(419-433)Online publication date: 6-Aug-2018
https://doi.org/10.1080/01621459.2017.1411269
Gossmann ABrzyski DSu WBogdan M(2016)grpSLOPE: Group Sorted L1 Penalized EstimationCRAN: Contributed Packages10.32614/CRAN.package.grpSLOPEOnline publication date: 24-Apr-2016
https://doi.org/10.32614/CRAN.package.grpSLOPE

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