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Current single-cell visualisation techniques project high dimensional data into ‘map’ views to identify high-level structures such as cell clusters and trajectories. New tools are needed to allow the transversal through the high dimensionality of single-cell data to explore the single-cell local neighbourhood. StarmapVis is a convenient web application displaying an interactive downstream analysis of single-cell expression or spatial transcriptomic data. The concise user interface is powered by modern web browsers to explore the variety of viewing angles unavailable to 2D media. Interactive scatter plots display clustering information, while the trajectory and cross-comparison among different coordinates are displayed in connectivity networks. Automated animation of camera view is a unique feature of our tool. StarmapVis also offers a useful animated transition between two-dimensional spatial omic data to three-dimensional single cell coordinates. The usability of StarmapVis is demonstrated by four data sets, showcasing its practical usability. StarmapVis is available at: https://holab-hku.github.io/starmapVis.

This review examines two important aspects that are central to modern big data bioinformatics analysis – software scalability and validity. We argue that not only are the issues of scalability and validation common to all big data bioinformatics analyses, they can be tackled by conceptually related methodological approaches, namely divide-and-conquer (scalability) and multiple executions (validation). Scalability is defined as the ability for a program to scale based on workload. It has always been an important consideration when developing bioinformatics algorithms and programs. Nonetheless the surge of volume and variety of biological and biomedical data has posed new challenges. We discuss how modern cloud computing and big data programming frameworks such as MapReduce and Spark are being used to effectively implement divide-and-conquer in a distributed computing environment. Validation of software is another important issue in big data bioinformatics that is often ignored. Software validation is the process of determining whether the program under test fulfils the task for which it was designed. Determining the correctness of the computational output of big data bioinformatics software is especially difficult due to the large input space and complex algorithms involved. We discuss how state-of-the-art software testing techniques that are based on the idea of multiple executions, such as metamorphic testing, can be used to implement an effective bioinformatics quality assurance strategy. We hope this review will raise awareness of these critical issues in bioinformatics.