Crisan, A. | bioRxiv Science

bioRxiv2018

An method for systematically surveying data visualizations in infectiousdisease genomic epidemiology

MotivationData visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.\n\nResultsWe have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations, and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.\n\nAvailabilityOur browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease

bioinformatics↗

bioRxiv2018

Adjutant: an R-based tool to support topic discovery for systematic and literature reviews

SummaryAdjutant is an open-source, interactive, and R-based application to support mining PubMed for a systematic or a literature review. Given a PubMed-compatible search query, Adjutant downloads the relevant articles and allows the user to perform an unsupervised clustering analysis to identify data-driven topic clusters. Users can also sample documents using different strategies to obtain a more manageable dataset for further analysis. Adjutant makes explicit trade-offs between speed and accuracy, which are modifiable by the user, such that a complete analysis of several thousand documents can take a few minutes. All analytic datasets generated by Adjutant are saved, allowing users to easily conduct other downstream analyses that Adjutant does not explicitly support.\n\nAvailability and ImplementationAdjutant is implemented in R, using Shiny, and is available at https://github.com/amcrisan/Adjutant

bioinformatics↗

bioRxiv2017

Evidence-Based Design and Evaluation of a Whole Genome Sequencing Clinical Report for the Reference Microbiology Laboratory

BackgroundMicrobial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics - including clinicians, laboratorians, epidemiologists, and researchers - is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results.\n\nMethodsWe used Design Study Methodology - a human centered multi-stage approach drawn from the information visualization domain - to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report.\n\nResultsWe recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type). When we compared our four prototype reports against the existing design, we found that for the majority (86.7%) of design comparisons, participants preferred the alternative prototype designs over the existing version, and that both clinicians and non-clinicians expressed similar design preferences. Participants articulated clearer design preferences when asked to compare individual design elements versus entire reports. Both the quantitative and qualitative data informed the design of a revised report, which is available online as a LaTeX template.\n\nConclusionsWe show how a human-centered design approach integrating quantitative and qualitative feedback can be used to design an alternative report for representing complex microbial genomic data. We suggest experimental and design guidelines to inform future design studies in the bioinformatics and microbial genomics domains, and suggest that this type of mixed-methods study is important to facilitate the successful translation of pathogen genomics in the clinic, not only for clinical reports but also more complex bioinformatics data visualization software.

genomics↗