Convert a twomodeClustering object to a data.frame

Description

This function creates a data.frame representation of a twomodeClustering object, listing the cluster assignments for both rows and columns.

Usage

## S3 method for class 'twomodeClustering'
as.data.frame(x, row.names = NULL, optional = FALSE, myMatrix = NULL, ...)

Arguments

Value

A data.frame with columns: name, type (row/col), and cluster assignment.

Integer mutation for genetic algorithm

Description

Performs mutation on a genetic algorithm individual by randomly changing cluster assignments with a specified probability.

Usage

gaintegerMutation(object, parent, ...)

Arguments

Value

Numeric vector representing the mutated individual.

One-point crossover for genetic algorithm with integer encoding

Description

Performs one-point crossover between two parent individuals in the genetic algorithm, exchanging genetic material at a single randomly selected point.

Usage

gaintegerOnePointCrossover(object, parents, ...)

Arguments

Value

List containing:

children

Matrix with two rows representing the offspring

fitness

Vector of NA values (fitness will be calculated later)

Integer population initialization for genetic algorithm

Description

Generates an initial population for the genetic algorithm where each individual represents a clustering solution with integer cluster assignments.

Usage

gaintegerPopulation(object, ...)

Arguments

Value

Matrix where each row represents an individual in the population and each column represents a cluster assignment.

Two-point crossover for genetic algorithm with integer encoding

Description

Performs two-point crossover between two parent individuals in the genetic algorithm, exchanging genetic material between two randomly selected points.

Usage

gaintegerTwoPointCrossover(object, parents, ...)

Arguments

Value

List containing:

children

Matrix with two rows representing the offspring

fitness

Vector of NA values (fitness will be calculated later)

Two mode clustering monitoring function factory for GA progress

Description

Creates a monitoring function that prints the current generation and the best fitness score to the console at specified intervals. Intended for use as a monitor function in GA runs.

Usage

monitorFactory(interval = 100)

Arguments

Value

A monitoring function that can be used with GA. The returned function takes a GA object and prints progress information at the specified interval.

Examples

# Create monitor that prints every 100 generations (default)
monitor <- monitorFactory()
# ga(..., monitor = monitor)

# Create monitor that prints every 50 generations
monitor <- monitorFactory(50)
# ga(..., monitor = monitor)

Plot two-mode clustering results (validation-aware, compact labels)

Description

Heatmap of the clustered matrix with clear cluster boundaries. If result$validation is present, each block shows one label with the chosen value plus significance stars.

Usage

plotTwomodeClustering(
  myMatrix,
  result,
  title = "",
  xlabel = "",
  ylabel = "",
  varOrder = 0,
  objOrder = 0,
  palette = c("diverging", "viridis", "grey"),
  showBoundaries = TRUE,
  boundaryColor = "white",
  boundarySize = 1,
  showMeans = TRUE,
  fixAspect = TRUE,
  showValidation = TRUE,
  value = c("mean", "standardized", "effectSS"),
  digits = 2,
  sigLevels = c(0.001, 0.01, 0.05, 0.1),
  showMarginal = TRUE,
  labelColor = "white",
  showGlobal = TRUE
)

Arguments

Value

A ggplot object.

Examples

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,
  elitism = 1,
  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
)

Print method for summary.twomodeClustering objects

Description

Prints key information about a two-mode clustering result, including matrix dimensions, cluster sizes, fitness, and (if available) validation highlights.

Usage

## S3 method for class 'summary.twomodeClustering'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print method for twomodeClustering objects

Description

Prints a concise summary of a twomodeClustering object, including matrix dimensions, cluster counts, fitness, and (if available) validation results.

Usage

## S3 method for class 'twomodeClustering'
print(x, ...)

Arguments

Value

Invisibly returns x.

Summary method for twomodeClustering objects

Description

Creates a summary of a twomodeClustering object, including matrix dimensions, cluster sizes, fitness, optional bicluster summaries (if matrix available), and optional validation highlights (if validation is present).

Usage

## S3 method for class 'twomodeClustering'
summary(object, ...)

Arguments

Value

An object of class summary.twomodeClustering with components:

matrixDim

Named integer vector: rows, cols

nRowClusters

Number of row clusters

nColClusters

Number of column clusters

rowClusterSizes

Table of row cluster sizes

colClusterSizes

Table of column cluster sizes

biclusters

Data frame with bicluster summaries (if myMatrix present), possibly merged with validation per-block stats

fitness

Best fitness value if available, else NA

validationGlobal

List with r2, fStat, pValue, dfModel, dfResid, pMonteCarlo (if present), or NULL

nSigBlocks

Number of BH-significant blocks at 0.05 if available, else NULL

rowContribution

Data frame with total effectSS per row cluster (if available), else NULL

colContribution

Data frame with total effectSS per column cluster (if available), else NULL

Two-mode clustering using genetic algorithm (with optional validation)

Description

Performs two-mode clustering on a numeric matrix using a genetic algorithm. The algorithm simultaneously clusters rows and columns to minimize within-cluster sum of squared errors (SSE). Optionally, a validation step is executed that tests the statistical significance of the found partition using validateTwomodePartition().

Usage

twomodeClusteringGA(
  myMatrix,
  nColClusters,
  nRowClusters,
  seeds = 1:5,
  verbose = FALSE,
  maxiter = 2000,
  popSize = 300,
  pmutation = 0.05,
  pcrossover = 0.5,
  elitism = 100,
  interval = 100,
  parallel = FALSE,
  run = NULL,
  validate = FALSE,
  validateCenter = TRUE,
  validatePerBlock = TRUE,
  validateMonteCarlo = 0L,
  validateFixBlockSizes = TRUE,
  validateStoreNull = FALSE,
  validateSeed = NULL
)

Arguments

Details

The function runs multiple GA instances with different random seeds and returns the best solution. The fitness function minimizes the sum of squared errors within clusters. Row and column clusters are optimized simultaneously.

Value

A list of class "twomodeClustering" containing:

bestGa

The best GA object from all runs

bestFitness

Best fitness value achieved (negative SSE)

bestSeed

Seed that produced the best result

rowClusters

Integer vector of row cluster assignments

colClusters

Integer vector of column cluster assignments

control

List of control parameters used

validation

List returned by validateTwomodePartition() if validate=TRUE; otherwise NULL

References

Hageman, J. A., van den Berg, R. A., Westerhuis, J. A., van der Werf, M. J., & Smilde, A. K. (2008). Genetic algorithm based two-mode clustering of metabolomics data. Metabolomics, 4, 141–149. doi:10.1007/s11306-008-0105-7

See Also

ga for the underlying genetic algorithm implementation

Examples

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,
  elitism = 1,
  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
)

Two-mode clustering genetic algorithm evaluation function (fast, robust)

Description

Fast evaluation of a two-mode clustering solution.

Usage

twomodeFitnessFactory(myMatrix)

Arguments

Value

Function(string, ...) -> numeric fitness value = negative SSE (higher is better).

Toy matrix with one multiplicative and one additive bicluster

Description

A small 12×9 matrix with a 2 x 3 two-mode cluster structure to demonstrate twomodeclusteringGA in a controlled setting.

Usage

data(twomodeToy)

Format

A numeric matrix of dimension 12 \times 9 with a 2 x 3 two-mode cluster structure

Examples

data("twomodeToy")
str(twomodeToy)
image(t(twomodeToy))

Validate a two-mode clustering partition by global and per-block significance

Description

Given a numeric matrix and a full two-mode partition (exclusive row and column clusters), this function tests whether the fitted block-means model explains more structure than expected under a no-structure null. The global test uses an F-statistic based on SS_fit and SSE derived from your fitness definition. Optionally, it also reports per-block chi-square tests and a fast Monte Carlo p-value using random partitions (no GA reruns).

Usage

validateTwomodePartition(
  myMatrix,
  rowClusters,
  colClusters,
  center = TRUE,
  perBlock = TRUE,
  monteCarlo = 0,
  fixBlockSizes = TRUE,
  storeNull = FALSE,
  seed = NULL
)

Arguments

Value

A list of class "twomodeValidation" with elements:

• nR, nC, kR, kC

• dfModel, dfResid

• ssTot, ssFit, sse, sigma2Hat, r2

• fStat, pValue (global F test)

• perBlock (data.frame with per-block stats) if perBlock=TRUE

• mc (list with nSim, pMonteCarlo, fNull or fNullQuantiles) if monteCarlo>0

Examples

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,
  elitism = 1,
  validate = FALSE,
  verbose = TRUE
)

result$validation <- validateTwomodePartition(myMatrix_s, 
                                rowClusters=result$rowClusters, 
                                colClusters=result$colClusters)

#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
)