In the age of high-throughput genomics, microarray technology dominated the market of high-throughput gene expression profiling for over a decade until the introduction of RNA-seq technology. Gene expression analysis is one of the most popular analyses in the field of biomedical research.
Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering. The new features of “heatmap3” include highly customizable legends and side annotation, a wider range of color selections, new labeling features which allow users to define multiple layers of phenotype variables, and automatically conducted association tests based on the phenotypes provided. The “heatmap3” package is developed based on the “heatmap” function in R, and it is completely compatible with it. The “heatmap3” package allows users to produce highly customizable state of the art heat maps and dendrograms. To tackle the limitations of the “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap” function by adding several more powerful and convenient features. However, the “heatmap” function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms.
Simple clustering and heat maps can be produced from the “heatmap” function in R.
Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control.
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