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.

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. To capture the benefit of all three ontologies,
  I developed 'HTGM3D', a three-dimensional version of 'GoMiner'.

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 now present an enhancement of the existing two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'),
  which is called 'HTGM4D'. The original 'HTGM2D' is augmented by adding two instances of the original
  'GoMiner' genes versus categories heatmaps, aligned with the categories axes of the 'HTGM2D' heatmap.