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

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

Human fetoplacental arterial and venous endothelial cells are differentially programmed by gestational diabetes mellitus, resulting in cell-specific barrier function changes

AIMS/HYPOTHESIS: An adverse intrauterine environment can result in permanent changes in the physiology of the offspring and predispose to diseases in adulthood. One such exposure, gestational diabetes mellitus (GDM), has been linked to development of metabolic disorders and cardiovascular disease in offspring. Epigenetic variation, including DNA methylation, is recognised as a leading mechanism underpinning fetal programming and we hypothesised that this plays a key role in fetoplacental endothelial dysfunction following exposure to GDM. Thus, we conducted a pilot epigenetic study to analyse concordant DNA methylation and gene expression changes in GDM-exposed fetoplacental endothelial cells. METHODS: Genome-wide methylation analysis of primary fetoplacental arterial endothelial cells (AEC) and venous endothelial cells (VEC) from healthy pregnancies and GDM-complicated pregnancies in parallel with transcriptome analysis identified methylation and expression changes. Most-affected pathways and functions were identified by Ingenuity Pathway Analysis and validated using functional assays. RESULTS: Transcriptome and methylation analyses identified variation in gene expression linked to GDM-associated DNA methylation in 408 genes in AEC and 159 genes in VEC, implying a direct functional link. Pathway analysis found that genes altered by exposure to GDM clustered to functions associated with 'cell morphology' and 'cellular movement' in healthy AEC and VEC. Further functional analysis demonstrated that GDM-exposed cells had altered actin organisation and barrier function. CONCLUSIONS/INTERPRETATION: Our data indicate that exposure to GDM programs atypical morphology and barrier function in fetoplacental endothelial cells by DNA methylation and gene expression change. The effects differ between AEC and VEC, indicating a stringent cell-specific sensitivity to adverse exposures associated with developmental programming in utero. DATA AVAILABILITY: DNA methylation and gene expression datasets generated and analysed during the current study are available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ) under accession numbers GSE106099 and GSE103552, respectively

Correction: High Keratin 8/18 Ratio Predicts Aggressive Hepatocellular Cancer Phenotype12 (vol 12, pg 256, 2019)

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