The Essential Toolbox of Data Science: Python, R, Git, and Docker

The daily work in data science involves a set of essential tools: the programming languages Python and R, the version control tool Git and the virtualization tool Docker. Proficiency in at least one programming language is required for data science. R is tied to a computing environment that focuses on statistics, in which many new algorithms in genomics and biomedicine are first published. Python has a root in system administration, and is a superb language for general programming. Version control is critical to managing complex projects, even if software development is not involved. Docker container is becoming a key tool for deployment, portability, and reproducibility. This chapter provides a self-contained practical guide of these topics so that readers can use it as a reference and to plan their training.

Primer on Reproducible Research in R: Enhancing Transparency and Scientific Rigor

Achieving research reproducibility is a precarious aspect of scientific practice. However, many studies across disciplines fail to be fully reproduced due to inadequate dissemination methods. Traditional publication practices often fail to provide a comprehensive description of the research context and procedures, hindering reproducibility. To address these challenges, this article presents a tutorial on reproducible research using the R programming language. The tutorial aims to equip researchers, including those with limited coding knowledge, with the necessary skills to enhance reproducibility in their work. It covers three essential components: version control using Git, dynamic document creation using rmarkdown, and managing R package dependencies with renv. The tutorial also provides insights into sharing reproducible research and offers specific considerations for the field of sleep and chronobiology research. By following the tutorial, researchers can adopt practices that enhance the transparency, rigor, and replicability of their work, contributing to a culture of reproducible research and advancing scientific knowledge.

The Drosophila tumour suppressor Lgl and Vap33 activate the Hippo pathway through a dual mechanism

The tumour suppressor, Lethal (2) giant larvae [Lgl; also known as L(2)gl], is an evolutionarily conserved protein that was discovered in the vinegar fly Drosophila, where its depletion results in tissue overgrowth and loss of cell polarity. Lgl links cell polarity and tissue growth through regulation of the Notch and the Hippo signalling pathways. Lgl regulates the Notch pathway by inhibiting V-ATPase activity via Vap33. How Lgl regulates the Hippo pathway was unclear. In this current study, we show that V-ATPase activity inhibits the Hippo pathway, whereas Vap33 acts to activate Hippo signalling. Vap33 physically and genetically interacts with the actin cytoskeletal regulators RtGEF (Pix) and Git, which also bind to the Hippo protein (Hpo) and are involved in the activation of the Hippo pathway. Additionally, we show that the ADP ribosylation factor Arf79F (Arf1), which is a Hpo interactor, is involved in the inhibition of the Hippo pathway. Altogether, our data suggest that Lgl acts via Vap33 to activate the Hippo pathway by a dual mechanism: (1) through interaction with RtGEF, Git and Arf79F, and (2) through interaction and inhibition of the V-ATPase, thereby controlling epithelial tissue growth.

The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R

Transparency and reproducibility are major prerequisites for conducting meaningful real-world evidence (RWE) studies that are fit for decision-making. Many advances have been made in the documentation and reporting of study protocols and results, but the principles for version control and sharing of analytic code in RWE are not yet as established as in other quantitative disciplines like computational biology and health informatics. In this practical tutorial, we aim to give an introduction to distributed version control systems (VCS) tailored toward the FAIR (Findable, Accessible, Interoperable, and Reproducible) implementation of RWE studies. To ease adoption, we provide detailed step-by-step instructions with practical examples on how the Git VCS and R programming language can be implemented into RWE study workflows to facilitate reproducible analyzes. We further discuss and showcase how these tools can be used to track changes, collaborate, disseminate, and archive RWE studies through dedicated project repositories that maintain a complete audit trail of all relevant study documents.