ABSTRACT
BACKGROUND: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. RESULTS: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. CONCLUSIONS: spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.
Subject(s)
Ecosystem , Transcriptome , Genomics , SoftwareABSTRACT
Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.