BioMedVis Curriculum Repository
Repository of teaching material for bio and medical data visualization
Welcome to the Repository of Teaching Materials for Vis in Biology and Medicine. We curate the repository for all of us who teach data visualization and specialize in training students in the life sciences.
How can I contribute?
Prepare any training materials you have (PDF, slides) in a single file. If there are multiple files, zip them together. Prepare the title, a short description and possibly a nice representative figure and send everything to Kay Nieselt ([email protected]), CC Blaž Zupan ([email protected]). If the files are large, you can send us a link.
Chapter 1: Fundamentals
The training material in this section should provide an overview of visualizations and visual analysis tools for biological data that adhere to the principles and best practices of data visualization.
Fundamentals of Data Visualization, by Kay Nieselt, presents visualizations and visual analysis tools for biological data that adhere to the fundamentals and best practices of data visualisation is presented. We will cover visualisation principles starting from single genes, to genomes, to multiple sequence alignments, phylogenies, networks and quantitative omics data.
Truthful Data Visualization, by Helena Jambor. Just as much as students need to learn about visual perception and design, as well as specific visualizations for biology and medicine applications, they also should be introduced to "Ethics", i.e. how not to mislead with visualizations. This lecture covers a few examples of misleading charts and diagrams. The examples should be discussed with students and if possible they may do a re-design homework for one or two of the examples. This lecture could be expanded with misleading iterative design choices, examples from medicine, including color in PET scans and survival plots uncertainty, and also with misleading examples of (microscopy) image data.
Chapter 2: Biological Data
Visualisation of Pangenomes, by Kay Nieselt, presents visualisations of pangenome, the term that was originally proposed to denominate collections of genomic sequences jointly analyzed or used as a reference. While in metagenomics, the aim is to understand the composition and operation of complex microbial consortia in environmental samples, the aim of pangenomics is to study the composition of genomes of members of one organism. While originally a pan-genome has been referred to as the full complement of genes in a species, this has recently been generalized to considering a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference rather than a single genome. This presentation will show different approaches to pangenome visualization. These approaches are categorized into gene-centric and sequence-centric approaches. While gene-centric approaches focus on gene content, the central emphasis of sequence-centric approaches are genome variation graphs.
Visualisation of Gene Expression Data, by Kay Nieselt, which presents the approaches to visualize large-scale expression data, also called transcriptome data. Starting from the central dogma of molecular biology, a brief overview of the two most commonly used technologies to collect transcriptome data is presented. We then turn to the challenges of visualising transcriptome data and then show, along an adapted transcriptome data type taxonomy, in the Shneiderman sense, typical expression data visualisations.
Chapter 3: Medical Data
TBD
Chapter 4: Data Visualisation
TBD