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$optimalKResults)
Super classes
jmvcore::Analysis -> ihcadvancedBase -> ihcadvancedClass
Methods
Inherited methods
jmvcore::Analysis$.createImage()jmvcore::Analysis$.createImages()jmvcore::Analysis$.createPlotObject()jmvcore::Analysis$.getSessionTemp()jmvcore::Analysis$.load()jmvcore::Analysis$.render()jmvcore::Analysis$.save()jmvcore::Analysis$.savePart()jmvcore::Analysis$.setCheckpoint()jmvcore::Analysis$.setParent()jmvcore::Analysis$.setReadDatasetHeaderSource()jmvcore::Analysis$.setReadDatasetSource()jmvcore::Analysis$.setResourcesPathSource()jmvcore::Analysis$.setStatePathSource()jmvcore::Analysis$addAddon()jmvcore::Analysis$asProtoBuf()jmvcore::Analysis$asSource()jmvcore::Analysis$check()jmvcore::Analysis$init()jmvcore::Analysis$optionsChangedHandler()jmvcore::Analysis$postInit()jmvcore::Analysis$print()jmvcore::Analysis$readDataset()jmvcore::Analysis$run()jmvcore::Analysis$serialize()jmvcore::Analysis$setError()jmvcore::Analysis$setStatus()jmvcore::Analysis$translate()ihcadvancedBase$initialize()