Published in

Oxford University Press, Bioinformatics, 10(33), p. 1572-1574, 2017

DOI: 10.1093/bioinformatics/btw837

Links

Tools

Export citation

Search in Google Scholar

Better diagnostic signatures from RNAseq data through use of auxiliary co-data

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Abstract Summary 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. Availability and Implementation 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. Supplementary information Supplementary data are available at Bioinformatics online.