ROC.voting: ROC curve based on the voting system for k-TSP method

Description

This function creates a ROC object used by the function ROC.graphic.ktsp() to plot a ROC curve based on a cutoff on the majority of voting system.

Usage

ROC.voting(dat, grp, n = 200, healthy = NULL, seed = NULL, para1 = 200, para2 = 1, length = 40, display = FALSE, med = FALSE)

Arguments

dat

Can either be (a) a matrix of m lines (the gene expressions) and n columns (the observations) or (b) an eSet object.

grp

Can either be (a) a character (or numeric) vector indicating the group of each observations or (b) an integer indicating the column of pData(dat) that represents the group of the observations.

The number of bootstrap resample that should be used to compute the ROC curve.

healthy

This variable is used to determine which group will be considerer as the healthy group (specificity). Need to give the label of the group.

seed

If a seed should be set or not.

para1

This paramater is used to control the smoothness of the ROC curves computed on the bootstrap.

para2

This paramater is also used to control the smoothness of the ROC curves computed on the bootstrap.

length

This paramters allows the used to control the length of the list used in the C code.

display

If warnings should be displayed or not.

med

If the mean of the median between the two groups for each gene should be substracted to the dataset or not.

Value

spec: A n by 2(para1+para2)+1 matrix representing the sensitivities achieved among the boostraps.
sens: A n by 2(para1+para2)+1 matrix representing the specificities achieved among the boostraps.
n: The number of boostrap resample used.

Details

The parameters para1 and para2 are used to make the ROC curve smoother. The way of computing the ROC curve is based on a cutoff on the voting system. This procedure is described in Damond et al. (paper is submitted in Bioinformatic, December 2011).

References

D. Geman, C. d'Avignon, D. Naiman and R. Winslow, "Classifying gene expression profiles from pairwise mRNA comparisons," Statist. Appl. in Genetics and Molecular Biology, 3, 2004.

A.C. Tan, D.Q. Naiman, L. Xu, R.L. Winslow, D. Geman, "Simple decision rules for classifying human cancers from gene expression profiles," Bioinformatics, 21: 3896-3904, 2005.

J. Damond, supervised by S. Morgenthaler and S. Hosseinian, "Presentation and study of robustness for several methods to classify individuals based on their gene expressions", Master thesis, Swiss Federal Institute of Technology Lausanne (Switzerland), 2011. J. Damond, S. Morgenthaler, S. Hosseinian, "The robustness of the TSP and the k-TSP and the computation of ROC curves", paper is submitted in Bioinformatics, December 2011.

Jeffrey T. Leek (). tspair: Top Scoring Pairs for Microarray Classification. R package version 1.10.0.

Examples

Run this code

  ## Not run: 
#   ## Load data
#   data(ktspdata) 
#   roc <- ROC.voting(dat, grp, n=20, healthy="healthy")
# ## End(Not run)

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