How to Create a Publication-Quality Heatmap in Python
<p>Heatmaps can be used as informative figures to convey quantitative data. They can be used to convey data in an easy-to-read format providing a concise data summary.</p>
<p>Python has a number of tools to facilitate the production of publication quality heatmaps. These include the Seaborn and Matplotlib libraries, in addition to the subplot2grid libraries which can provide a convenient way to organise data in a heatmap.</p>
<p>In this tutorial, I will detail the steps required to produce a heatmap which focuses on the presence/absence of key elements. To do this, I will use a CSV file containing fictitious data about a selection of bacterial isolates. These bacterial strains have a number of features including antibiotic resistance genes, virulence genes, and certain capsule types. A heatmap will allow the quick inspection and comparison between the various strains.</p>
<p>While the example used focuses on bacterial strains, the techniques applied can be used more broadly for other datasets to help you visualised your data using a heatmap. Throughout the following tutorial, all images are by the author.</p>
<h2>Objective</h2>
<p>To create a publication quality heatmap displaying the presence/absence of key genes from fictitious bacterial strains.</p>
<p>This tutorial will use the following csv file, ‘Bacterial_strain_heatmap_tutorial_data.csv’ available from the Github repository.</p>
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