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

Statistical Applications in Genetics and Molecular Biology - Tập 14 Số 3 - 2015

Michael G. Schimek¹, Eva Budínská², Karl Kugler³, Vendula Švendová¹, Jie Ding⁴, Shili Lin⁵

¹Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria

²Bioinformatics in Translational Research, IBA, Masaryk University, Kotlarska 2, 61137 Brno, Czech Republic

³Institute for Bioinformatics and Systems Biology, Helmholtz Centre Munich, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany

⁴Stanford Cancer Institute, Stanford University, 265 Campus Drive, Stanford, CA 94305-5456, USA

⁵Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus OH 43210, USA

Tóm tắt

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

Từ khóa

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