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The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software., Source code for article available at https://github.com/nuest/rockerverse-paper/ Updated version includes some new paragraphs and corrections throughout the text; full diff available at https://github.com/nuest/rockerverse-paper/compare/preprint.v2...preprint.v3

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta., Author summary Gene expression quantification from RNA sequencing is a common component of many research publications. In order that research findings can be computationally reproducible, it is critical that gene expression datasets are linked to the correct gene annotation, including the source of the annotation, the release number, and the location of the genes in a particular genome assembly. Often it is difficult for this critical metadata to be found for public datasets, and manually curating this information subjects the process to human error. We describe a solution for the missing metadata problem, whereby we embed a checksum of the RNA reference sequences in the output files during the expression quantification step. Later we use this checksum for identification and automatic attachment of the correct metadata when the dataset is loaded into R for statistical analysis. We feel this paradigm of embedded checksums and subsequent metadata retrieval will also prove useful in other computational biology contexts.

We introduce and examine dbEmpLikeGOF, an R package for performing goodness-of-fit tests based on sample entropy. This package also performs the two sample distribution comparison test. For a given vector of data observations, the provided function dbEmpLikeGOF tests the data for the proposed null distributions, or tests for distribution equality between two vectors of observations. The proposed methods represent a distribution-free density-based empirical likelihood technique applied to nonparametric testing. The proposed procedure performs exact and very efficient p values for each test statistic obtained from a Monte Carlo (MC) resampling scheme. Note by using an MC scheme, we are assured exact level α tests that approximate nonparametrically most powerful Neyman-Pearson decision rules. Although these entropy based tests are known in the theoretical literature to be very efficient, they have not been well addressed in statistical software. This article briefly presents the proposed tests and introduces the package, with applications to real data. We apply the methods to produce a novel analysis of a recently published dataset related to coronary heart disease.

The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose “SmoothArray”, a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package “aCGHplus,” this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.

While the technologies for high dimensional data have been advancing, a lack of adequate visualisation tools to accommodate the results and inability to integrate multiple sources of data has emerged. The move towards multi-disciplinary work and collaborative research impresses the need for visualisation and analysis tools that are platform independent and customisable. iGenomicViewer through the use of customisable tool-tips that may include links and images, allows for a greater level of data integration for genomic data in a variety of formats. The iGenomicViewer is a freely available R software which allows users to generate interactive, platform-independent plots of genomic data.