Advanced IHC Clustering Analysis

Advanced clustering analysis with marker optimization and validation. Includes optimal K selection, PCA analysis, and iterative marker selection.

Usage

ihcadvanced(
  data,
  markers,
  id = NULL,
  optimalKMethod = "silhouette",
  kRange = "2:8",
  iterativeClustering = FALSE,
  pcaAnalysis = TRUE,
  consensusClustering = FALSE,
  nBootstrap = 100,
  clusterValidation = TRUE,
  showAdvancedPlots = TRUE,
  parallelProcessing = FALSE,
  randomSeed = 123
)

Arguments

data

the data as a data frame

markers

Select IHC marker variables (e.g., ER, PR, HER2, Ki67, CD markers). Can be categorical scores (0/1+/2+/3+) or continuous values (H-scores, percentages).

id

Case identifier for tracking

optimalKMethod

Method for automatically determining optimal number of clusters

kRange

Range of K values to test (e.g., "2:8" or "2,3,4,5")

iterativeClustering

Perform iterative optimization to select most informative markers

pcaAnalysis

Perform PCA for dimensionality reduction and visualization

consensusClustering

Use bootstrap consensus clustering for stable results

nBootstrap

Number of bootstrap iterations for consensus clustering

clusterValidation

Perform multiple validation metrics (silhouette, connectivity, Dunn index)

showAdvancedPlots

Display PCA, silhouette, and validation plots

parallelProcessing

Enable parallel processing for computationally intensive tasks

randomSeed

Set random seed for reproducible results

Value

A results object containing:

results$instructions					a html
results$assumptions					a html
results$optimalKResults					a table
results$markerOptimization					a table
results$pcaResults					a table
results$pcaLoadings					a table
results$validationMetrics					a table
results$consensusResults					a table
results$optimalKPlot					an image
results$pcaPlot					an image
results$silhouettePlot					an image
results$validationPlot					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$optimalKResults$asDF

as.data.frame(results$optimalKResults)

Examples