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

 Type: Package

 Title: Evaluation of Bioinformatics Metrics

 URL: https://github.com/neobernad/evaluomeR

-Version: 1.21.1

+Version: 1.21.2

 Authors@R: c(

     person("José Antonio", "Bernabé-Díaz", email = "[email protected]", role = c("aut", "cre")),

     person("Manuel", "Franco", email = "[email protected]", role = "aut"),

@@ -156,8 +156,19 @@ clusterbootWrapper <- function(data, B, bootmethod="boot",

 clusteringWrapper <- function(data, cbi, krange, seed, ...) {

   cbiHelperResult = helperGetCBI(cbi, krange, ...)

-  old.seed <- .Random.seed

-  on.exit( { .Random.seed <<- old.seed } )

+  if(exists(".Random.seed")){

+    # .Random.seed might not exist when launched as background job

+    # so only store and reset if it exists

+    old.seed <- .Random.seed

+  }

+

+  on.exit(

+    {

+      if(exists("old.seed")) {

+        .Random.seed <<- old.seed

+      }

+    }

+  )

   if (!is.null(seed)) set.seed(seed)

@@ -702,7 +702,7 @@ standardizeStabilityData <- function(stabData, k.range=NULL) {

 #' @examples

 #' data("ontMetrics")

 #' annotated_clusters=annotateClustersByMetric(ontMetrics, k.range=c(2,3), bs=20, seed=100)

-#' View(annotated_clusters[['ANOnto']])

+#' annotated_clusters[['ANOnto']]

 annotateClustersByMetric <- function(df, k.range, bs, seed){

   if (is.null(seed)) {

     seed = pkg.env$seed

@@ -775,8 +775,8 @@ annotateClustersByMetric <- function(df, k.range, bs, seed){

 #'

 #' @examples

 #' data("ontMetrics")

-#' ranges = getMetricRangeByCluster(ontMetrics, k.range=c(2,3), bs=20, seed=100)

-#' View(ranges)

+#' #ranges = getMetricRangeByCluster(ontMetrics, k.range=c(2,3), bs=20, seed=100)

+

 getMetricRangeByCluster <- function(df, k.range, bs, seed) {

   if (is.null(seed)) {

     seed = pkg.env$seed

@@ -172,7 +172,8 @@ qualityRange <- function(data, k.range=c(3,5), cbi="kmeans", getImages=FALSE,

 #' @references

 #' \insertRef{kaufman2009finding}{evaluomeR}

 #'

-qualitySet <- function(data, k.set=c(2,4), cbi="kmeans", getImages=FALSE, seed=NULL, ...) {

+qualitySet <- function(data, k.set=c(2,4), cbi="kmeans", all_metrics=FALSE,

+                       getImages=FALSE, seed=NULL, ...) {

   k.set.length = length(k.set)

   if (k.set.length == 0) {

@@ -188,7 +189,8 @@ qualitySet <- function(data, k.set=c(2,4), cbi="kmeans", getImages=FALSE, seed=N

   data <- as.data.frame(assay(data))

   suppressWarnings(

-    runQualityIndicesSilhouette(data, bs = 1, seed=seed, cbi=cbi, k.set=k.set, ...))

+    runQualityIndicesSilhouette(data, bs = 1, seed=seed, cbi=cbi, all_metrics=all_metrics,

+                                k.set=k.set, ...))

   silhouetteData =  suppressWarnings(

     runSilhouetteTableRange(data, k.set=k.set))

@@ -2,15 +2,15 @@ library(evaluomeR)

 data("rnaMetrics")

-dataFrame <- stability(data=rnaMetrics, k=4, bs=100, getImages = FALSE)

-dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,4), bs=20, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, k=4, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,4), bs=20, all_metrics = FALSE, getImages = FALSE)

 assay(dataFrame)

 # Metric    Mean_stability_k_2  Mean_stability_k_3  Mean_stability_k_4

 # [1,] "RIN"     "0.825833333333333" "0.778412698412698" "0.69625"

 # [2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333"

-dataFrame <- stabilitySet(data=rnaMetrics, k.set=c(2,3,4), bs=20, getImages = FALSE)

+dataFrame <- stabilitySet(data=rnaMetrics, k.set=c(2,3,4), bs=20, all_metrics = FALSE, getImages = FALSE)

-dataFrame <- quality(data=rnaMetrics, cbi="kmeans", k=3, getImages = FALSE)

+dataFrame <- quality(data=rnaMetrics, cbi="kmeans", k=3, all_metrics = FALSE, getImages = FALSE)

 assay(dataFrame)

 # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore

 # [1,] "RIN"     "0.420502645502646" "0.724044583696066" "0.68338517747747"

@@ -18,7 +18,7 @@ assay(dataFrame)

 # Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size

 # [1,] "0.627829396038413"  "4"            "4"            "8"

 # [2,] "0.737191191352892"  "8"            "5"            "3"

-dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,4), seed = 20, getImages = FALSE)

+dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,4), seed = 20, all_metrics = FALSE, getImages = FALSE)

 assay(getDataQualityRange(dataFrame, 2))

 # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Avg_Silhouette_Width Cluster_1_Size

 # 1 "RIN"     "0.583166775069983" "0.619872562681118" "0.608402004052639"  "5"

@@ -36,24 +36,25 @@ assay(getDataQualityRange(dataFrame, 4))

 # Cluster_4_Size

 # 1 "5"

 # 2 "3"

-dataFrame1 <- qualitySet(data=rnaMetrics, k.set=c(2,3,4), getImages = FALSE)

+dataFrame1 <- qualitySet(data=rnaMetrics, k.set=c(2,3,4), all_metrics = FALSE, getImages = FALSE)

 dataFrame <- metricsCorrelations(data=rnaMetrics, getImages = FALSE, margins = c(4,4,11,10))

 assay(dataFrame, 1)

-dataFrame <- stability(data=rnaMetrics, cbi="kmeans", k=2, bs=100, getImages = FALSE)

-dataFrame <- stability(data=rnaMetrics, cbi="clara", k=2, bs=100, getImages = FALSE)

-dataFrame <- stability(data=rnaMetrics, cbi="clara_pam", k=2, bs=100, getImages = FALSE)

-dataFrame <- stability(data=rnaMetrics, cbi="hclust", k=2, bs=100, getImages = FALSE)

-dataFrame <- stability(data=rnaMetrics, cbi="pamk", k=2, bs=100, getImages = FALSE)

-dataFrame <- stability(data=rnaMetrics, cbi="pamk_pam", k=2, bs=100, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="kmeans", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="clara", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="clara_pam", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="hclust", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="pamk", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="pamk_pam", k=2, bs=100, all_metrics = FALSE, getImages = FALSE)

+dataFrame <- stability(data=rnaMetrics, cbi="rskc", k=2, bs=100, all_metrics = TRUE, L1 = 2, alpha=0, getImages = FALSE)

 # Supported CBIs:

 evaluomeRSupportedCBI()

-dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,10), getImages = FALSE)

+dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,10), all_metrics = FALSE, getImages = FALSE)

 dataFrame

 #dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,8), bs=20, getImages = FALSE)

@@ -8,7 +8,7 @@ cluster = plotMetricsCluster(ontMetrics, scale = TRUE)

 plotMetricsViolin(rnaMetrics)

 plotMetricsViolin(ontMetrics, 2)

-ntMetricsstabilityData <- stabilityRange(data=rnaMetrics, k.range=c(3,4), bs=20, getImages = FALSE, seed=100)

+stabilityData <- stabilityRange(data=rnaMetrics, k.range=c(3,4), bs=20, getImages = FALSE, seed=100)

 qualityData <- qualityRange(data=rnaMetrics, k.range=c(3,4), getImages = FALSE, seed=100)

 kOptTable <- getOptimalKValue(stabilityData, qualityData, k.range=c(3,4))

@@ -33,3 +33,4 @@ test = qualityRange(data=ontMetrics, k.range=c(3,3),

 # Shows how clusters are partitioned according to the individuals

 individuals_per_cluster(test$k_3)

+

@@ -1,7 +1,7 @@

 library(evaluomeR)

 library(RSKC)

 library(sparcl)

-

+seed = 100

 dataFrame <- quality(data=ontMetrics, cbi="kmeans", k=3)

 assay(dataFrame)

 # Metric     Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore  Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size

deleted file mode 100755

@@ -1,123 +0,0 @@

-library(evaluomeR)

-library(RSKC)

-library(sparcl)

-

-

-# Dataframe for the use case is 'ontMetrics' provided by our evaluomeR package.

-

-data("ontMetrics")

-df = as.data.frame(assay(ontMetrics))

-df["Description"] = NULL # Description column not relevant atm.

-head(df, 5)

-data("ontMetrics")

-

-# RSKC

-# Robust and Sparse K-Means clustering [[1]](#1) requires to select mainly three parameters:

-# - **nlc**: Number of *K* cluster. It is to be determined by *evaluomeR* optimal *K* algorithm.

-#   - **L<sub>1</sub>**: The tuning parameter for sparce clustering. It acts as the upper bound restraint for the vector of weights. 1 $<$ L<sub>1</sub> $\leq$ $\sqrt{num.variables}$.

-#   - **$\alpha$**: The trimming portion [[4]](#4) used in the robust clustering.

-

-# Optimal K clusters value

-# Here, we make use of *evaluomeR* to figure out the optimal $k$ value. The algorithm on how the optimal is calculated is outlined in [[7]](#7). We consider the $k$ range [3,15] for the analysis of the optimal $k$, avoiding $k=2$ to prevent from having binary classifications.

-seed=100

-k.range=c(3,15)

-stabilityData <- stabilityRange(data=ontMetrics, k.range=k.range, bs=20, getImages = FALSE, seed=seed)

-qualityData <- qualityRange(data=ontMetrics, k.range=k.range, getImages = FALSE, seed=seed)

-optK <- getOptimalKValue(stabilityData, qualityData, k.range=k.range)

-

-# Optimal $k$ values individually per input metric are:

-optK[c('Metric','Global_optimal_k')]

-

-k_values = as.numeric(unlist(optK['Global_optimal_k']))

-global_k_value = floor(mean(k_values))

-print(paste0("Taking global optimal K value: ", global_k_value))

-

-plotMetricsClusterComparison(ontMetrics, k.vector1=global_k_value)

-

-# Figuring out the L1 upper boundry

-# In [[2]](#2) authors provide description of the algorithm to select the tunning parameter L<sub>1</sub> for

-# the sparse K-means, which consist of independent permutations from the same source data matrix and the gap

-# statistic [[5]](#5). This algorithm for tuning the L<sub>1</sub> parameter and others described in [[2]](#2)

-# are presented in 'sparcl' R package [[6]](#6).

-

-dataMatrix = as.matrix(df)

-dataMatrix = scale(dataMatrix, TRUE, TRUE)

-head(dataMatrix, 5)

-

-# Considering that for the dataset the global optimal $k$ is $k=4$, we can now compute the

-# permutations to figure out the boundry L<sub>1</sub> with the method 'KMeansSparseCluster.permute'

-# from 'sparcl' [[6]](#6).

-

-# Note: 1 $<$ L<sub>1</sub> $\leq$ $\sqrt{num.variables}$.

-

-wbounds = seq(2,sqrt(ncol(dataMatrix)), len=30)

-km.perm <- KMeansSparseCluster.permute(dataMatrix,K=global_k_value,wbounds=wbounds,nperms=5)

-print(km.perm)

-plot(km.perm)

-

-l1 = km.perm$bestw

-print(paste0("Best L1 upper bound is: ", l1))

-

-

-

-# Metrics relevancy

-rskc_out = RSKC(df["ANOnto"], global_k_value, 0.1, L1 = l1, nstart = 200,

-                silent=TRUE, scaling = FALSE, correlation = FALSE)

-cat(paste0("L1 value: ", l1,"\n"))

-cat(names(rskc_out$weights)[1], ": ", rskc_out$weights[1],"\n")

-cat(names(rskc_out$weights)[2], ": ", rskc_out$weights[2],"\n")

-cat(names(rskc_out$weights)[3], ": ", rskc_out$weights[3],"\n")

-cat("---\n")

-

-rskc_out

-

-# Trimmed cases:

-

-# oE: Indices of the cases trimmed in squared Euclidean distances.

-# oW:	Indices of the cases trimmed in weighted squared Euclidean distances. If L1 =NULL,

-# then oW are the cases trimmed in the Euclidean distance, because all the features have the same weights, i.e., 1's.

-union_vector = c(rskc_out$oE,rskc_out$oW)

-union_vector_unique = unique(union_vector)

-union_vector_unique = sort(union_vector_unique)

-

-print(paste0("Trimmed cases from input dataframe: "))

-union_vector_unique

-

-options(scipen=10)

-

-columns = c('metric', 'weight')

-rskc_df = data.frame(matrix(ncol = length(columns), nrow = length(rskc_out$weights)))

-colnames(rskc_df) = columns

-rskc_df['metric'] = names(rskc_out$weights)

-rskc_df['weight'] = rskc_out$weights

-rskc_df

-

-# Relevancy table

-rskc_df_sorted = rskc_df[order(rskc_df$weight, decreasing = TRUE), ]

-rskc_df_sorted

-

-# References <a class="anchor" id="references"></a>

-

-#<a id="1">[1]</a>

-#  Kondo, Y., Salibian-Barrera, M., & Zamar, R. (2016). RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm. Journal of Statistical Software, 72(5), 1–26. https://doi.org/10.18637/jss.v072.i05

-

-#<a id="2">[2]</a>

-#  Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726. https://doi.org/10.1198/jasa.2010.tm09415

-

-#<a id="3">[3]</a>

-#  Robert Tibshirani, & Guenther Walther (2005). Cluster Validation by Prediction Strength. Journal of Computational and Graphical Statistics, 14(3), 511-528. https://doi.org/10.1198/106186005X59243

-

-#<a id="4">[4]</a>

-#  Gordaliza, A. (1991). On the breakdown point of multivariate location estimators based on trimming procedures. Statistics & Probability Letters, 11(5), 387-394. https://doi.org/10.1016/0167-7152(91)90186-U

-

-#<a id="5">[5]</a>

-#  Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. https://doi.org/10.1111/1467-9868.00293

-

-#<a id="6">[6]</a>

-#  Witten, D. M., & Tibshirani, R. (2010). sparcl: Perform Sparse Hierarchical Clustering and Sparse K-Means Clustering. R package. https://CRAN.R-project.org/package=sparcl

-

-#<a id="7">[7]</a>

-#  José Antonio Bernabé-Díaz, Manuel Franco, Juana-María Vivo, Manuel Quesada-Martínez, & Jesualdo T. Fernández-Breis (2022). An automated process for supporting decisions in clustering-based data analysis. Computer Methods and Programs in Biomedicine, 219, 106765. https://doi.org/10.1016/j.cmpb.2022.106765

-

-

-