Heatmaps are a great way of visualizing your count data across multiple conditions. This can be represented as counts per sample, average counts per group, or log2FC values vs control per group. This can be a great way to demonstrate both inter- and intra-group differences in your data.
The data for heatmaps can come from any type of count data. A few examples are RNA-seq normalized hit counts, Luminex assay counts, plant petal size/color, average expression values per sample, log2FC values for multiple conditions, etc. Generally we recommend scaling your data by row (each gene/value), though if you are visualizing something like log2FC values we would recommend no scaling.
To use this tool, at minimum you must have a tsv/csv table containing with the first row containing your row identifiers (for example Gene IDs), followed by a column with count data per sample (for example VST normalized hit counts). Additionally you need a metadata table. The first column should contain the sample names matching the columns in the count data. Each subsequent column can include any non-measured data, such as grouping variables.