resampleORCME: Estimation of the proportion of the heterogeneity in the observed data for clustering

Description

The function is computing within cluster sum of squares for given proportion of heterogeneity. Minimal number of genes per cluster is fixed as 2. The sum of squares is computed through resampling the 100 data sets with 100 genes randomly sampled with replacement from the reduced expression data.

Usage

resampleORCME(clusteringData, lambdaVector, robust=FALSE)

Arguments

clusteringData

the microarray data with rows corresponding to genes and columns corresponding to time points or different doses

lambdaVector

vector of assumed proportions of of heterogeneity of the observed data, it ranges between 0 and 1. A lambda value of 1 considers the observed data as a cluster and lambda value of 0 finds every possible pattern within the data

robust

logical variable that determines, if algorithm uses robust version based on median polish and absolute values, instead of mean square error. Default is FALSE.

Value

lambda: vector of the proportions of heterogeneity given as input
WSS: within clusters sum of squares for given proportion of heterogeneity
TSS: total clusters sum of squares for given proportions of heterogeneity
nc: number of clusters as a function for given proportions of heterogeneity

References

Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.

Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.

Examples

Run this code

  data(doseData)
  data(geneData)

  dirData <- monotoneDirection(geneData = geneData,doseData = doseData)
  incData <- as.data.frame(dirData$incData)
 
  lambdaVector <- c(0.05,0.50,0.95)
  
  
  resampleORCME(clusteringData=incData, lambdaVector=lambdaVector, robust=FALSE)