5-9 July 2021 | Online
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8A - Statistics and Bioinformatics
Bioinformatics / Biomedical or health informatics, Biostatistics and Epidemiology, Web Applications (Shiny/Dash), Data visualisation
Session Sponsor: Roche Session Slides
1:45pm - 2:05pm
ID: 113 / ses-08-A: 1
Topics: Bioinformatics / Biomedical or health informatics
Keywords: computational biology, bioinformatics, molecular evolution, phylogeny, sequence analysis, R Shiny
MolEvolvR: Web-app and R-package for characterizing proteins using molecular evolution and phylogeny
Michigan State University, United States of America
Molecular evolution and phylogeny can provide key insights into pathogenic protein families. Studying how these proteins evolve across bacterial lineages can help identify lineage-specific and pathogen-specific signatures and variants, and consequently, their functions. We have developed a streamlined computational approach for characterizing the molecular evolution and phylogeny of target proteins, widely applicable across proteins and species of interest. Our approach starts with query protein(s) of interest, identifying their homologs, and characterizing each protein by its domain architecture and phyletic spread. We have developed the MolEvolvR webapp, written entirely in R and Shiny, to enable biologists to run our entire workflow on their data by simply uploading a list of their proteins of interest. The webapp accepts inputs in multiple formats: protein/domain sequences, multi-protein operons/homologous proteins, or motif/domain scans. Depending on the input, MolEvolvR returns the complete set of homologs/phylogenetic tree, domain architectures, common partner domains. Users can obtain graphical summaries that include multiple sequence alignments and phylogenetic trees, domain architectures, domain proximity networks, phyletic spreads, co-occurrence patterns, and relative occurrences across lineages. Thus, the MolEvolvR webapp provides a powerful, easy-to-use interface for a wide range of protein characterization analyses, starting from homology searches and phylogeny to domain architectures. In addition to this analysis, researchers can use the app for data summarization and dynamic visualization. The webapp can be accessed here: http://jravilab.org/molevolvr. Soon, it will be available as an R-package for use by computational biologists.
Link to package or code repository.
2:05pm - 2:25pm
ID: 201 / ses-08-A: 2
Topics: Web Applications (Shiny/Dash)
Keywords: API, cloud computing, web application, applications/case studies, plumber
Using R to Empower a Precision Dosing Web Application
Rx Studio Inc., United States of America
R has a long history in PK/PD modeling, and it has been heavily used both in research and clinical practice as well, but making these R packages available outside of the R community has its (technical, compliance and UX) challenges that even hosted Shiny apps cannot easily solve yet. We are working on and presenting a scalable platform and web application building on the top of R (Docker, containerized Plumber API), hosted in a HIPAA-compliant infrastructure (AWS and GCP services), and made available to end-users via a user-friendly and configurable web interface (Angular and Nebular). This talk will focus on the overall cloud infrastructure, how we integrate R and other services, and details on the troubles with scalability, error handling, user experience etc in a HIPAA compliant, but startup environment.
2:25pm - 2:45pm
ID: 136 / ses-08-A: 3
Topics: Data visualisation
Visualization of highly-multiplexed imaging data with cytomapper
University of Zurich
Highly multiplexed imaging (HMI) produces images that contain up to 40 channels. In the field of cell biology, HMI is used to capture differences between individual cells, which are defined as distinct objects on the images. To derive those objects from multi-channel images, different segmentation approaches can be used. Several challenges in terms of data visualisation arise from this type of high dimensional data: 1. more than 3 channels need to be visualised at once, 2. the features of segmented objects need to be visualised together with pixel-level information and 3. tens to hundreds of images need to be visualised in parallel. Here, we have developed cytomapper, an R/Bioconductor package to address these challenges. The main functions of the package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of object-level information on segmentation masks and 3. the interactive visualization of images based on an integrated shiny application. Finally, we also developed an on-disk representation framework to expand the usability of the package to several hundreds of images.
2:45pm - 3:05pm
ID: 346 / ses-08-A: 4
Topics: Biostatistics and Epidemiology
Analyzing Clinical Trials Data using R for Exploratory and Regulatory Analyses
The R language has gained traction in the Pharmaceutical industry to analyze clinical trials data. Especially shiny apps have proven to be useful for exploratory analyses. We will present two frameworks to analyze clinical trials data: one to create interactive shiny apps and another one for static output generation. In our demonstration we will especially focus on modularity and reproducibility.