twomodeclusteringGA

twomodeclusteringGA is an R package for performing two-mode clustering (biclustering) using a Genetic Algorithm (GA). The package provides a flexible and efficient framework to detect row and column clusters simultaneously in numeric data matrices.

Key features:

• GA-based optimization tailored for two-mode clustering
• Built-in visualization tools for clustered heatmaps
• Functions for summarizing, comparing, and exporting results
• Easy-to-use workflow suitable for small and medium-sized datasets

Installation

You can install the development version directly from GitHub:

# install.packages("devtools")
# devtools::install_github("joshageman/twomodeclusteringGA")
install.packages("twomodeclusteringGA", repos = c('https://joshageman.r-universe.dev', 'https://cloud.r-project.org'))

Example

A toy dataset is included to demonstrate the functionality:

data("twomodeToy")
myMatrix_s <- scale(twomodeToy)

# Run the GA-based two-mode clustering
result <- twomodeClusteringGA(
  myMatrix     = myMatrix_s,
  nRowClusters = 2,
  nColClusters = 3,
  seeds        = 1,
  maxiter      = 200,
  popSize      = 30,
  validate     = TRUE,
  verbose      = TRUE
)

# Inspect the result
print(result)
summary(result)
myTwomodeResult <- as.data.frame(result)
head(myTwomodeResult)

# Plot the clustered heatmap
plotTwomodeClustering(
  myMatrix  = myMatrix_s,
  result    = result,
  title     = "Two-mode clustering Toy example",
  fixAspect = FALSE
)

References

This package builds on the literature on biclustering and genetic algorithms. Key references:

• Hageman, J. A., Wehrens, R., & Buydens, L. M. C. (2008). Simplivariate Models: Ideas and First Examples. PLoS ONE, 3(9), e3259. doi:10.1371/journal.pone.0003259
• Madeira, S. C., & Oliveira, A. L. (2004). Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1(1), 24–45. doi:10.1109/TCBB.2004.2

Contributing

Contributions, suggestions, and bug reports are welcome. Please open an issue or submit a pull request.

Author

Maintainer: Jos Hageman (jos.hageman@wur.nl) Wageningen University & Research