@@ -2,7 +2,7 @@ Package: omada

 Type: Package

 Title: Machine learning tools for automated transcriptome 

     clustering analysis

-Version: 0.99.12

+Version: 0.99.13

 Authors@R: person("Sokratis Kariotis", "Developer", role = c("aut", "cre"),

                      email = "[email protected]")

 Description: Symptomatic heterogeneity in complex diseases reveals differences 

@@ -13,9 +13,7 @@

 #'

 #' @examples

 #' clusteringMethodSelection(toy_genes, method.upper.k = 3,

-#' number.of.comparisons = 4)

-#' clusteringMethodSelection(toy_genes, method.upper.k = 2,

-#' number.of.comparisons = 2)

+#' number.of.comparisons = 3)

 #'

 #' @import ggplot2

 #' @importFrom clValid clusters

@@ -13,7 +13,6 @@

 #' @export

 #'

 #' @examples

-#' feasibilityAnalysisDataBased(data = toy_genes, classes = 3)

 #' feasibilityAnalysisDataBased(data = toy_genes, classes = 2)

 #'

 #' @importFrom fpc speccCBI

@@ -14,8 +14,7 @@

 #' @export

 #'

 #' @examples

-#' feasibilityAnalysis(classes = 3, samples = 320, features = 400)

-#' feasibilityAnalysis(classes = 4, samples = 400, features = 120)

+#' feasibilityAnalysis(classes = 2, samples = 20, features = 30)

 #'

 #' @importFrom fpc speccCBI

@@ -13,7 +13,6 @@

 #' @export

 #'

 #' @examples

-#' featureSelection(toy_genes, min.k = 3, max.k = 9, step = 3)

 #' featureSelection(toy_genes, min.k = 2, max.k = 4, step = 4)

 #'

 #' @importFrom fpc speccCBI

@@ -5,7 +5,7 @@

 #' @export

 #'

 #' @examples

-#' fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+#' fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 #' average.sts.k <- get_average_stabilities_per_k(fa.object)

 get_average_stabilities_per_k <- function(object) {

     UseMethod("get_average_stabilities_per_k")

@@ -5,7 +5,7 @@

 #' @export

 #'

 #' @examples

-#' fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+#' fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 #' average.st <- get_average_stability(fa.object)

 get_average_stability <- function(object) {

     UseMethod("get_average_stability")

@@ -5,7 +5,7 @@

 #' @export

 #'

 #' @examples

-#' fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+#' fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 #' maximum.st <- get_max_stability(fa.object)

 get_max_stability <- function(object) {

     UseMethod("get_max_stability")

@@ -16,7 +16,6 @@

 #' @export

 #'

 #' @examples

-#' omada(toy_genes, method.upper.k = 5)

 #' omada(toy_genes, method.upper.k = 3)

 omada <- function(data, method.upper.k = 5) {

@@ -11,7 +11,6 @@

 #' @export

 #'

 #' @examples

-#' optimalClustering(toy_genes, 4,"spectral")

 #' optimalClustering(toy_genes, 2,"kmeans")

 #'

 #' @importFrom fpc speccCBI hclustCBI kmeansCBI

@@ -24,8 +24,6 @@ Method Selection through intra-method Consensus Partition Consistency

 }

 \examples{

 clusteringMethodSelection(toy_genes, method.upper.k = 3,

-number.of.comparisons = 4)

-clusteringMethodSelection(toy_genes, method.upper.k = 2,

-number.of.comparisons = 2)

+number.of.comparisons = 3)

 }

@@ -26,7 +26,6 @@ Simulating dataset and calculate stabilities over different number of

 clusters

 }

 \examples{

-feasibilityAnalysis(classes = 3, samples = 320, features = 400)

-feasibilityAnalysis(classes = 4, samples = 400, features = 120)

+feasibilityAnalysis(classes = 2, samples = 20, features = 30)

 }

@@ -25,7 +25,6 @@ Simulating dataset based on existing dataset's dimensions, mean and standard

 deviation

 }

 \examples{

-feasibilityAnalysisDataBased(data = toy_genes, classes = 3)

 feasibilityAnalysisDataBased(data = toy_genes, classes = 2)

 }

@@ -25,7 +25,6 @@ the selected features

 Predictor variable subsampling sets and bootstrapping stability set selection

 }

 \examples{

-featureSelection(toy_genes, min.k = 3, max.k = 9, step = 3)

 featureSelection(toy_genes, min.k = 2, max.k = 4, step = 4)

 }

@@ -16,6 +16,6 @@ Average stabilities for all numbers of clusters(k)

 Get average stabilities for all numbers of clusters(k)

 }

 \examples{

-fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 average.sts.k <- get_average_stabilities_per_k(fa.object)

 }

@@ -16,6 +16,6 @@ The average stability(over all k)

 Get the average stability(over all k)

 }

 \examples{

-fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 average.st <- get_average_stability(fa.object)

 }

@@ -16,6 +16,6 @@ The maximum stability

 Get the maximum stability

 }

 \examples{

-fa.object <- feasibilityAnalysis(classes = 4, samples = 50, features = 15)

+fa.object <- feasibilityAnalysis(classes = 2, samples = 10, features = 15)

 maximum.st <- get_max_stability(fa.object)

 }

@@ -27,6 +27,5 @@ A wrapper function that utilizes all tools to produce the optimal

 sample memberships

 }

 \examples{

-omada(toy_genes, method.upper.k = 5)

 omada(toy_genes, method.upper.k = 3)

 }

@@ -22,7 +22,6 @@ stability score and parameter used

 Clustering with the optimal parameters estimated by these tools

 }

 \examples{

-optimalClustering(toy_genes, 4,"spectral")

 optimalClustering(toy_genes, 2,"kmeans")

 }