Data Visualization for Genome Biology

In this module we'll cover 3 straightforward approaches for generating simple plots. We'll go over common biological data visualization paradigms, common chart types, and discuss the context and grammar of data visualization.

This module explores ways of displaying biological variation and background on track viewers. It covers visual perception, Gestalt principles, and color perception issues related to accessibility. The lab uses PlotsOfDifferences and R to generate charts displaying variation, and JBrowse for assessing gene expression levels.

This module explores ways of visualizing gene expression data, covering RNA-seq for measuring gene expression levels and statistical tests for identifying significantly differentially expressed genes. It also covers design thinking. The lab uses Galaxy for volcano plots and R for heatmaps of gene expression.

This module covers how the Gene Ontology can be used to make sense of long lists of genes from 'omic experiments, especially through Gene Ontology enrichment analyses. It also looks at Agile Development and User Testing for improving data visualization tools. The lab uses three online Gene Ontology analysis apps and R for creating overview charts.

This module explores tools for displaying and analyzing graph networks, notably those created when generating protein-protein interactions. It covers web services/APIs available for biology. The lab uses Cytoscape to explore interactors of BRCA2, BiNGO for Gene Ontology enrichment analyses, and D3 for displaying interaction networks.

This module covers methods for generating and making sense of large biological data sets. It introduces dimensionality reduction methods like t-SNE, PCA, and UMAP for visualizing sample similarities. It also covers logic diagrams (Venn-Euler or Upset plots) for displaying gene set similarities.