ORCME: Order restricted clustering for dose-response trends in microarray experiments

Description

The function performs delta-clustering of a microarray data. It can be used for clustering of both the time-course or dose-response microarray data.

Usage

ORCME(DRdata, lambda, phi, robust=FALSE)

Arguments

DRdata

matrix of a microarray data with rows corresponding to genes and columns corresponding to time points or different doses

lambda

assumed proportion of coherence relative to 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.

phi

minimum number of genes in a cluster

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

The matrix of classification into clusters: each row represents one gene and columns found clusters. The matrix consist of the Booleans values, in each row there is only one of them TRUE which means that the gene was classified into the respective cluster.

References

Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in EarlyDrug 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)
  
  
  print(orcme <- ORCME(DRdata=incData,lambda=0.15,phi=2))
  orcmeRobust <- ORCME(DRdata=incData,lambda=0.15,phi=2, robust=TRUE)
  
  # number of genes within cluster
  colSums(orcme)
  colSums(orcmeRobust)