Introduction

Our aim is to improve omics based prediction and feature selection using multiple sources of auxiliary information: co-data. Adaptive group regularized ridge regression (GRridge) was proposed to achieve this by estimating additional group-based penalty parameters through an empirical Bayes method at a low computational cost. We illustrate the GRridge method and software on RNA sequencing datasets. The method boosts the performance of an ordinary ridge regression and outperforms other classifiers. Post-hoc feature selection maintains the predictive ability of the classifier with far fewer markers.GRridge is an R package that includes a vignette. It is freely available at ( https://bioconductor.org/packages/GRridge/ ). All information and R scripts used in this study, including those on retrieval and processing of the co-data, are available from http://github.com/markvdwiel/GRridgeCodata .mark.vdwiel@vumc.nl.Supplementary data are available at Bioinformatics online.

Publications

  1. Better diagnostic signatures from RNAseq data through use of auxiliary co-data.
    Cite this
    Novianti PW, Snoek BC, Wilting SM, van de Wiel MA, 2017-05-01 - Bioinformatics (Oxford, England)
  2. Better prediction by use of co-data: adaptive group-regularized ridge regression.
    Cite this
    van de Wiel MA, Lien TG, Verlaat W, van Wieringen WN, Wilting SM, 2016-02-01 - Statistics in medicine

Credits

  1. Putri W Novianti
    Developer

    Department of Pathology, VU University Medical Center

  2. Barbara C Snoek
    Developer

    Department of Pathology, VU University Medical Center

  3. Saskia M Wilting
    Developer

    Department of Pathology, VU University Medical Center

  4. Mark A van de Wiel
    Investigator

    Department of Mathematics, VU University

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Summary
AccessionBT000187
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByMark A van de Wiel