cv2: Crossvalidation with chosen pairs of genes

In ktspair: k-Top Scoring Pairs for Microarray Classification

Description

This function computes the performance of the model where the pairs of genes has been selected by the user.

Usage

cv2(indice, dat, grp, cross = 5, display = FALSE, length = 40, seed = NULL, med = FALSE, healthy = NULL)

Arguments

indice	The pairs that should be used to construct the ktsp object
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.
cross	The number of fold that should be used in the crossvalidation.
display	Allows the user to avoid the function ktspcalc() to print waring message over the loop.
length	This paramters allows the used to control the length of the list used in the C code.
seed	Allow the user to set a seed.
med	If the mean of the median between the two groups for each gene should be substracted to the dataset or not.
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.

Details

This function is used to compute the performance reached by the model where the pairs of genes have been chosen by the user.

Value

k	The selected value for k
accuracy	The estimated percentage of correct prediction reached by the k-TSP with the chosen pairs of genes.
sensitivity	The estimated sensitivity reached by the k-TSP with the chosen pairs of genes.
specificity	The estimated specificity reached by the k-TSP with the chosen pairs of genes.

Author(s)

Julien Damond [e-mail]

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 <[e-mail]> (). tspair: Top Scoring Pairs for Microarray Classification. R package version 1.10.0.

See Also

ktspcalc2, cv

Examples

  ## Not run: 
  ## Load data
  data(ktspdata) 
  cv <- cv(dat, grp,cross =10)
  ktsp <- ktspcalc(dat, grp, cv$k)
  ktsp
  cv
 
## End(Not run)

Example output

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

k-TSP object with: 5 TSPs
Pair:		TSP Score		Indices
TSP 1 : 	 0.96 			 657 793 
TSP 2 : 	 0.92 			 74 704 
TSP 3 : 	 0.92 			 224 298 
TSP 4 : 	 0.92 			 588 758 
TSP 5 : 	 0.92 			 34 266 
$k
[1] 5

$accuracy_k
[1] 0.96

$accuracy
[1] 0.84 0.92 0.96 0.88 0.58

$sensitivity
[1] 0.8416667 0.9750000 1.0000000 0.9000000 0.6000000

$specificity
[1] 0.8240741 0.9074074 0.9444444 0.8611111 0.5750000

attr(,"class")
[1] "cv"

ktspair documentation built on May 2, 2019, 3:25 a.m.