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Introduction to Heatmap: A Visualized Tool for Microbial Community Composition Analysis

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Heatmap is a powerful tool for the visual display of microarray data or data from next-generation sequencing studies such as microbiome analysis. Heatmap is a graphical representation of data that uses a system of color-coding in representing different values contained in a matrix. As early as the 19th century, heatmaps were used in statistical analysis and progressed in 2008 as a useful tool for almost every field such as marketing, engineering, medicine, and even in research. Today, heatmaps are commonly used in showing user behavior on a specific webpage. Heatmap is also user-friendly, more importantly to those who are not accustomed to reading large quantities of data since it is more visually accessible than traditional data formats.

Heatmap is considered a useful tool because it can provide a comprehensive overview as its data visualization tools are easy to understand and are often self-explanatory. It is a lot different from a table or chart which both need to be interpreted or studied to be understood. Although, heatmap can also be misleading since it involves a large number of values or data which often results in the inconclusion of other necessary information needed in order to make an accurate assumption about its focus. Aside from that, heatmap can only show certain situations that happened. Heatmap cannot provide factors on why a certain situation is happening or give insights on the situation that is happening.

Use of Heatmaps In Bioinformatics

One of the many applications of heatmap is in the field of bioinformatics. Since it is a useful tool in analyzing and visualizing multi-dimensional datasets, it is usually used in studying samples with high-throughput gene expression data. It is mainly to locate hidden groups among analyzed genes. It is also used in associating experimental conditions to gene expression patterns.

Some studies have used heatmap as a method for visualizing and interpreting gene expression data obtained through microarray, as well as through targeted RNA-sequencing. In some instances, heatmap is combined with clustering method which groups genes together based on the similarity of their gene expression pattern. This association is very helpful in identifying genes that are biologically associated with a particular condition.

In the field of genetics, heat map is usually displayed in a grid wherein each row represents a gene while each column represents a sample. The changes in gene expression are represented by the differences in color and the intensity of the boxes.

Introduction to Heatmap: A Visualized Tool for Microbial Community Composition AnalysisFigure 1. An example of heatmap in microbial research. (Jumpstart Consortium Human Microbiome Project Data Generation Working Group, 2012)

Figure 1 illustrates the BLAST-based and RDP-based 16S rDNA heatmaps for microbial community profiling in human microbiome research.

Functional Heatmap

One of the automated and interactive tools used in gene pattern recognition is Functional Heatmap. It is specifically used in enhancing pattern recognition of time-series multi-omics assays. It is an interactive tool that provides an analytical framework without computational expertise. In fact, researchers find it easy to use since it reduces the manual labor of pattern discovery and comparison as it translates statistical models into visual clues that are easy to understand. It allows bioinformaticians to rapidly extract biological meaning while simultaneously create figures by using only a single tool. It was said to be universally compatible and also offers comprehensive gene expression analysis resources in high resolution to researchers. In a study of Williams and his colleagues in 2019, it is concluded that Function Heatmap is considered as a tool which has a great help in identifying hidden treads of changes in terms of multi-tissues or condition time-series omic assays in the future.


  1. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016, 32(18):2847-9.
  2. Williams JR, Yang R, Clifford JL, et al. Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays. BMC bioinformatics. 2019, 20(1):1-6.
  3. Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PloS one. 2012, 13;7(6):e39315.
* For Research Use Only. Not for use in diagnostic procedures.

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