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HTGM2D  

Two Dimensional High Throughput 'GoMiner'
View on CRAN: Click here


Download and install HTGM2D package within the R console
Install from CRAN:
install.packages("HTGM2D")

Install from Github:
library("remotes")
install_github("cran/HTGM2D")

Install by package version:
library("remotes")
install_version("HTGM2D", "1.0")



Attach the package and use:
library("HTGM2D")
Maintained by
Barry Zeeberg
[Scholar Profile | Author Map]
First Published: 2025-04-07
Latest Update: 2025-04-07
Description:
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.
How to cite:
Barry Zeeberg (2025). HTGM2D: Two Dimensional High Throughput 'GoMiner'. R package version 1.0, https://cran.r-project.org/web/packages/HTGM2D. Accessed 03 May. 2025.
Previous versions and publish date:
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Complete documentation for HTGM2D
Functions, R codes and Examples using the HTGM2D R package
Full HTGM2D package functions and examples
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