通过使用辅助共数据从RNA测序数据中获得更好的诊断特征。

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

作者信息

Novianti Putri W, Snoek Barbara C, Wilting Saskia M, van de Wiel Mark A

机构信息

Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.

Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.

出版信息

Bioinformatics. 2017 May 15;33(10):1572-1574. doi: 10.1093/bioinformatics/btw837.

DOI:10.1093/bioinformatics/btw837

PMID:28073760

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 .

CONTACT

mark.vdwiel@vumc.nl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

我们的目标是利用多种辅助信息源（共数据）改进基于组学的预测和特征选择。为此，我们提出了自适应组正则化岭回归（GRridge）方法，通过经验贝叶斯方法以较低的计算成本估计额外的基于组的惩罚参数。我们在RNA测序数据集上展示了GRridge方法和软件。该方法提高了普通岭回归的性能，并且优于其他分类器。事后特征选择使用少得多的标记物就能保持分类器的预测能力。