Related Experiment Video
Updated: Mar 9, 2026
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Better diagnostic signatures from RNAseq data through use of auxiliary co-data
Putri W Novianti1,2, Barbara C Snoek2, Saskia M Wilting2
1Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.
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 .
Contact:
mark.vdwiel@vumc.nl.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
