HCP: A Matlab package to create beautiful heatmaps with richly annotated covariates

Manuela Salvucci,Jochen Prehn

doi:10.21105/joss.01291

Abstract

HCP (HeatmapCovariatePlot) provides a simple high level application programming interface (API) to design elaborated visualizations in a modular fashion. The user can select which elements to include, covariate row annotations and/or heatmaps, by invoking the AddCovariateRow or the AddHeatmap methods. Elements can be vertically stacked and also grouped in functionally related sub-blocks encapsulated by the AddSubBlock method to adjust the figure layout. The plotting options in HCP are chosen sensibly to create production-quality out-of-the-box visualizations in most use-case. HCP features several plotting options to adjust the plot aesthetics to cater for the user preferences in terms of colormaps, labelling, legends and layouts (margins and positions). HCP ease-of-use and rapidity enables the users to iterate through multiple visualization alternatives while focusing on the message conveyed by the data rather than the technicalities involved in generating the plot.

Highlights

Heatmaps are often used in conjunction with cluster analysis to re-order observations and/or features by similarity and rendering common and distinct patterns more apparent
In the field of bioinformatics, heatmaps are frequently used to visualize high-throughput and high-dimensional datasets, such as those derived from profiling biological samples with -omic technologies
Biological samples are characterized at multiple -omic level and it is of interest to contrast and compare patterns captured at the different molecular layers along with their associations with other observable features

Summary

Introduction

Heatmaps are often used in conjunction with cluster analysis to re-order observations and/or features by similarity and rendering common and distinct patterns more apparent. It is often of interest to interpret the underlying patterns in the context of other data sources. Biological samples (for example, patient tumour samples) are characterized at multiple -omic level and it is of interest to contrast and compare patterns captured at the different molecular layers along with their associations with other observable features (covariates).

Results

Conclusion