Data Visualization

This collection covers visualization strategies for communicating results and exploring high-dimensional omics data. Effective visualization serves dual purposes: enabling discovery through exploratory analysis and clearly communicating findings to diverse audiences. We document best practices for creating publication-quality figures, interactive exploratory plots, and analytical visualizations that incorporate their own computational methods.

The following pages cover visualization approaches organized by analytical purpose and/or data type, including dimensionality reduction techniques for data exploration, statistical result summaries, genomic data browsers, and matrix visualizations for pattern detection.

For context on generating the underlying data for these visualizations, see our sections on Analysis of Omics Data by Assay, Differential Analysis of Omics Data, and Biological Interpretation of Results.