Heatmaps are one of the most commonly used representation tools for synthesis of complex pools of data. By coding numerical values into colors, heatmaps enable quick representation of quantitative differences in expression levels of biological data.
Using heatmaps to visualize your data
Heatmaps are particularly useful for analysis of gene expression microarray data. Most heatmap representations are also combined with clustering methods to group genes and/or samples based on their expression patterns. Each gene is represented as a row and is color-coded to represent the intensity of its variation (either positive or negative) relative to a reference value. Biological samples are represented as columns in the grid.
To evaluate the OMICtools series of data visualization tools, we asked members of the OMICtools community to vote for their favorite heatmap generation tools. Here are the top 3 heatmap generation tools, selected by 63 voters.
Your number 1 tool: Heatmapper
67% of you chose Heatmapper as your number 1 favorite tool to generate heatmaps.
The Heatmapper software is a versatile tool that allows you to create a wide variety of heatmaps for many different data types, such as heatmaps for transcriptomic, proteomic and metabolomic data but also pairwise distance maps, image overlay heatmaps or geopolitical heatmaps.
You can upload your data as text, Excel, or tab delimited formatted tables and export the resulting heatmaps in various formats.
Being one of the few web-based and generalist heatmap representation tools, Heatmapper is particularly suited for users with low computational power or for non-specialists. Complex sets of data from microarray, RNA-seq, proteomic or metabolomic experiments can also be displayed and clustered using one of the five distance measurement methods available, including Pearson and Spearman Rank correlation.
Your second best heatmap generation software: Gitools
48% of the OMICtools community voted for Gitools as the best tool for generating heatmaps.
This software enables analysis and visualization of genomic data as interactive heatmaps. After uploading your data, Gitools lets you choose between different types of analyses over matrices and modules, such as enrichment analysis, correlations and overlaps. One of its unique features, Oncodrive, is a method to identify genes which are more altered than would be expected by chance, taking into account the whole matrix. The originality of the Gitools software lies in its capacity to navigate data and results in the form of interactive heatmaps and obtain detailed information by clicking on each cell of the map.
Practical tutorials, as well as examples of data and results are provided on the Gitools main website, and explanatory videos can be found on the Barcelona Biomedical Genomics Lab Youtube channel. Example of an explanatory video for Gitools about sorting and stratifying heatmaps.
Third place goes to Shinyheatmap
In third position, Shinyheatmap was chosen by 35% of voters.
It is designed as a user-friendly heatmap software and has a low memory footprint, which enables interactive visualization of very large datasets. Shinyheatmap also features a built-in high performance web plug-in fastheatmap, that can compute datasets of millions of rows within seconds. As such, it is particularly suited for RNA-seq or NGS-driven studies. Shinyheatmap can generate both static and interactive heatmaps and allows the user to customize several parameters.
Our next OMICtools survey on data visualization will focus on Venn diagram tools – it’s your chance to have your say (you’ll receive a survey invite by email)!
(Babicki et al., 2016) Heatmapper: Web-enable heat mapping for all. Nucleic Acids Research.
(Perez-Llamas and Lopez-Bigas, 2011) Gitools: Analysis and visualization of genomic data using interactive heat-maps. PLoS ONE.
(Khomtchouk et al., 2017) shinyheatmap: Ultrafast low memory heatmap web interface for big data genomics. PLos ONE.