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

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.

Multi-omics characterization of interaction-mediated control of human protein abundance levels

“…The linear models were implemented using lm R function and the LRT test with associated statistics were calculated using lrtest function from lmtest R package. The Borda ranking method was implemented using the Borda function from TopKLists R package (Schimek et al, 2015) .…”

Analysis of ranking data

“…Other rank aggregation methods not considered here are online rank aggregation (Helmbold & Warmuth, 2007 from multiple omics ranked lists (Schimek et al, 2015), and an indirect inference approach for rank aggregation (Švendová & Schimek, 2017). For comparison of various aggregation methods, see Li, Wang, and Xiao (2019); Q.…”

Inferring a consensus problem list using penalized multistage models for ordered data

“…We describe here our computational approach for fitting penalized BPL models using seamless L 0 penalties. All code was written in the R statistical environment (R Core Team, 2018; Wickham, 2017;Neuwirth, 2014;Schimek et al, 2015) and is freely available via github (https://github.com/psboonstra/RankModeling). When g is an L 0 -type penalty, maximizing n i=1 log f i (β) − g(β, λ) is a non-convex optimization problem that is both computationally difficult and which admits the possibility of identifying local optima.…”