TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Schimek Michael G. (), Budinská Eva, Kugler Karl G., Švendová Vendula, Ding Jie and Lin Shili
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Schimek Michael G.: Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria
Budinská Eva: Bioinformatics in Translational Research, IBA, Masaryk University, Kotlarska 2, 61137 Brno, Czech Republic
Kugler Karl G.: Institute for Bioinformatics and Systems Biology, Helmholtz Centre Munich, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
Švendová Vendula: Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria
Ding Jie: Stanford Cancer Institute, Stanford University, 265 Campus Drive, Stanford, CA 94305-5456, USA
Lin Shili: Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 3, 311-316

Abstract: High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format – rankings – for the same set of items. Here we present the R package TopKLists, which allows for statistical inference on the lengths of informative (top-k) partial lists, for stochastic aggregation of full or partial lists, and for graphical exploration of the input and consolidated output. A graphical user interface has also been implemented for providing access to the underlying algorithms. To illustrate the applicability and usefulness of the package, we integrated microRNA data of non-small cell lung cancer across different measurement techniques and draw conclusions. The package can be obtained from CRAN under a LGPL-3 license.

Keywords: 62G99; 65K10; 68N01; 65C60; 62F07 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0093

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