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

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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 ( All information and R scripts used in this study, including those on retrieval and processing of the co-data, are available from


Supplementary information:

Supplementary data are available at Bioinformatics online.

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