Heatmap Generator

A gene expression heatmap is the workhorse figure of transcriptomics because it makes a matrix of genes by samples legible at a glance. Color encodes value, and once the rows and columns are ordered sensibly, co-regulated genes and related samples snap into visible blocks. The technique became standard after Eisen et al. (1998) paired hierarchical clustering with a colored grid to display genome-wide expression.

Two processing choices determine whether the figure is honest. The first is scaling. Genes span orders of magnitude in absolute expression, so without standardization a handful of highly expressed genes wash out everything else. Z-scoring each row, so color reflects deviation from that gene's own mean, is the convention for expression heatmaps and is why the scale here is diverging: red above the row mean, blue below.

The second is clustering. Hierarchical agglomerative clustering measures the distance between every pair of rows, then repeatedly merges the closest clusters, producing an ordering that places similar genes adjacent. Doing the same on columns groups similar samples. This tool clusters both with average linkage on Euclidean distance, the same defaults used by common heatmap packages, and lets you turn column clustering off when the sample order is meaningful, such as a time course.

A heatmap displays structure but does not test it: apparent blocks should be confirmed with a formal analysis, and the genes shown are usually a pre-selected significant set rather than the whole transcriptome. Select that set with the volcano plot generator and the false discovery rate calculator, normalize inputs with the RNA-seq normalization calculator, and for the complete analysis with a proper statistical model, the bioinformatics analysis service delivers the figure and the statistics behind it.