These functions compute the k best pairs of genes used to classify samples based on the relative rank of the genes expression within each profile. A score based on the sensitivity and the specificity is calculated for every possible pair. The k pairs with the highest score will be selected with the restriction that a gene can appear in at most one pair. The value of k is either set as a parameter chosen by the user or computed through crossvalidation. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
|Author||Julien Damond <email@example.com>|
|Date of publication||2011-12-23 10:06:13|
|Maintainer||Julien Damond <firstname.lastname@example.org>|
AUC.calc: Compute the AUC
bootstrap.graphic.ktsp: Graphical display of the bootstrap procedure
bootstrap.ktsp: Bootstrap procedure for the k-TSP
cv: Crossvalidation for the parameter k
cv2: Crossvalidation with chosen pairs of genes
dat: Simulated gene expressions of individuals.
eSet: Simulated gene expressions of individuals.
grp: Group of the individuals.
kts.pair: Calculation of the k top scoring pairs.
ktspair-package: Computation of the k-TSP
ktspcalc: Compute the k top scoring pairs based on a gene expression...
ktspcalc2: The k-TSP with chosen pairs of genes
ktspdata: Simulated dataset of gene expressions in a matrix form.
ktspplot: Graphical representation of ktsp objects
make.consecutive.int: Transform the group vector into a binary vector
ordertsp: Ordering of the pairs of genes
plot.ktsp: Graphical representation of ktsp objects
predict.ktsp: Prediction using a ktsp object
print.ktsp: Print the results of the k-TSP
rank_na: Rank the gene expression and Replace NA
ROC.graphic.ktsp: Graphical display of the ROC curve
ROC.offset: ROC curve based on the offset method for k-TSP method
ROC.voting: ROC curve based on the voting system for k-TSP method
summary.ktsp: Summary of ktsp object