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

Putri W. Novianti, Barbara C. Snoek, Saskia M. Wilting, Mark A. Van De Wiel

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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 .

Original languageEnglish
Pages (from-to)1572-1574
Number of pages3
JournalBioinformatics
Volume33
Issue number10
DOIs
Publication statusPublished - 15 May 2017

Cite this

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Better diagnostic signatures from RNAseq data through use of auxiliary co-data. / Novianti, Putri W.; Snoek, Barbara C.; Wilting, Saskia M.; Van De Wiel, Mark A.

In: Bioinformatics, Vol. 33, No. 10, 15.05.2017, p. 1572-1574.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Novianti, Putri W.

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AU - Van De Wiel, Mark A.

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AB - 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 .

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