Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. 
  (2003) <doi:10.1186/gb-2003-4-4-r28>) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005)
  <doi:10.1186/1471-2105-6-168>) implement 'GoMiner' and 'High Throughput GoMiner'
  ('HTGM') to map lists of genes to the Gene Ontology (GO) <https://geneontology.org>. Until recently,
  these were hosted on a server at The National Cancer Institute (NCI). In order to continue
  providing these services to the bio-medical community, I have developed stand-alone versions.
  The current package 'HTGM' builds upon my recent package 'GoMiner'.
  The output of 'GoMiner' is a heatmap showing the relationship of a single list
  of genes and the significant categories into which they map. 'High Throughput GoMiner'
  ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM'
  is a heatmap showing the relationship of the significant categories derived from each gene list.
  The heatmap has only 2 axes, so the identity of the genes are unfortunately
  "integrated out of the equation." Because the graphic for the heatmap is implemented
  in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each
  picture element to the relevant list of genes. By clicking on the desired picture
  element, the user can recover the "lost" genes.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. Microarray studies are usually analyzed with BP, whereas
  proteomics researchers often prefer CC. To capture the benefit of both of those ontologies,
  I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a
  2D heat map whose axes are any two of BP, MF, or CC, and the value within
  a picture element of the heat map reflects the Jaccard metric p-value for the number of genes
  in common for the corresponding pair.