ClustALL Package
2023-11-30
Contents
1 Intro
ClustAll is an R package originally developed for patient stratification in complex diseases.
ClustAll constitutes a stratification framework aim at identifying patient subgroups while addressing the most common challenges found when dealing with clinical data. The idea of this pipeline is that if a stratification is solid, it should be possible to arrive at such stratification through different clustering methods. To compute patient stratification, ClustAll combines different distance metrics (Correlation based-distance and Gower distance), as well as different clustering methods (K-Means, K-Medoids and H-Clust).
Furthermore, ClustAll:
- Allows to work with data with missing values, mixed data and
correlated variables.
- Provides more than a single stratification solution, unlike other
packages.
- It is population-based robust: ClustAll performs a population-based
robustness analysis for each candidate stratification using
bootstrapping. This analysis ensures that non-robust stratifications are
excluded.
- It is parameter-based robust: The selection of representative
stratifications enables the preservation of those stratifications that
demonstrate parameter-based robustness: consistency when depth, distance
metrics or clustering methods are altered.
In addition, the package offers functions to:
- Validate the output using the true labels.
- Show the patient flow
between clusters if two candidate stratifications are compared.
2 Setup
First, install the ClustAll package if is not already installed.
library(ClustAll)3 Examples on how to use ClustAll on Public Data Sets.
3.1 Breast Cancer Wisconsin (Diagnostic)
Input data for this example can be found on doi: 10.24432/C5DW2B
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. From 569 patients.
This dataset contains 30 variables (10x3), which consist of ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)In addition to the patient ID and the Diagnose (Diagnosis (M = malignant, B = benign)).
3.1.1 Loading and preparing data
data("BreastCancerWisconsin", package = "ClustAll") # load example data
# remove patients ID, as it is not a clinical variable
data_use <- subset(wdbc,select=-ID)
str(data_use)
#> 'data.frame': 569 obs. of 31 variables:
#> $ Diagnosis : chr "M" "M" "M" "M" ...
#> $ radius1 : num 18 20.6 19.7 11.4 20.3 ...
#> $ texture1 : num 10.4 17.8 21.2 20.4 14.3 ...
#> $ perimeter1 : num 122.8 132.9 130 77.6 135.1 ...
#> $ area1 : num 1001 1326 1203 386 1297 ...
#> $ smoothness1 : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
#> $ compactness1 : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
#> $ concavity1 : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
#> $ concave_points1 : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
#> $ symmetry1 : num 0.242 0.181 0.207 0.26 0.181 ...
#> $ fractal_dimension1: num 0.0787 0.0567 0.06 0.0974 0.0588 ...
#> $ radius2 : num 1.095 0.543 0.746 0.496 0.757 ...
#> $ texture2 : num 0.905 0.734 0.787 1.156 0.781 ...
#> $ perimeter2 : num 8.59 3.4 4.58 3.44 5.44 ...
#> $ area2 : num 153.4 74.1 94 27.2 94.4 ...
#> $ smoothness2 : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
#> $ compactness2 : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
#> $ concavity2 : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
#> $ concave_points2 : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
#> $ symmetry2 : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
#> $ fractal_dimension2: num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
#> $ radius3 : num 25.4 25 23.6 14.9 22.5 ...
#> $ texture3 : num 17.3 23.4 25.5 26.5 16.7 ...
#> $ perimeter3 : num 184.6 158.8 152.5 98.9 152.2 ...
#> $ area3 : num 2019 1956 1709 568 1575 ...
#> $ smoothness3 : num 0.162 0.124 0.144 0.21 0.137 ...
#> $ compactness3 : num 0.666 0.187 0.424 0.866 0.205 ...
#> $ concavity3 : num 0.712 0.242 0.45 0.687 0.4 ...
#> $ concave_points3 : num 0.265 0.186 0.243 0.258 0.163 ...
#> $ symmetry3 : num 0.46 0.275 0.361 0.664 0.236 ...
#> $ fractal_dimension3: num 0.1189 0.089 0.0876 0.173 0.0768 ...3.1.1.0.1 createClustAll
This function creates the class ClustAllObject. It is an S4 object
designed to apply the ClustAll algorithm and store the main results.
The usage and arguments are the following:
data_use - Data Frame with the original dataset. It may contain
missing values (NAs).
nImputation - Numeric value. Number of imputations to be computed in
case there original dataset contains NAs.
dataImputed - mids object created with mice R package. The data used
to compute the imputation and the data_used using must be the same.
There are 3 possible scenarios:
- The data does not contain NAs. nImputation & dataImputed are not
required
- The data contains NAs and the imputations are computed within
ClustAll framework (using mice package). nImputations is
required.
- The data contains NAs and the imputed data set (mid class) .
dataImputed is required.
3.1.2 a) Workflow with a dataset with no missing values
3.1.2.0.1 createClustAll
obj_noNA <- createClustAll(data = data_use, colValidation = "Diagnosis",
nImputation = NULL, dataImputed = NULL)
#> The dataset contains character values.
#> They are converted to categorical (more than one class) or to binary (one class).
#> Before continuing, check that the transformation has been processed correctly.
#>
#> ClustALL object created successfully. You can use runClustAll.3.1.2.0.2 runClustAll method
This function computes the ClustAll algorithm.
The usage and arguments are the following:
Object - ClustAllObject-class object. threads - Numeric value.
Number of cores to use (for parallel computing).
simplify - Logical value. If TRUE only 1 out of 4 depths of the
dendrogram are considered; if FALSE all possible depths of the
dendrogram are considered.
obj_noNA1 <- runClustAll(Object = obj_noNA, threads = 8, simplify = FALSE)
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!
obj_noNA1simplify <- runClustAll(Object = obj_noNA, threads = 8,
simplify = TRUE)
#> CAUTION! The simplify parameter is set to TRUE.
#> For more robust results consider changing the simplify parameter to FALSE.
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!
tNOsimplify <- system.time(runClustAll(Object = obj_noNA, threads = 8,
simplify = FALSE))
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!
tYESsimplify <- system.time(runClustAll(Object = obj_noNA, threads = 8,
simplify = TRUE))
#> CAUTION! The simplify parameter is set to TRUE.
#> For more robust results consider changing the simplify parameter to FALSE.
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!print(tNOsimplify)
#> user system elapsed
#> 68.560 2.201 94.801
print(tYESsimplify)
#> user system elapsed
#> 17.347 1.143 35.180Once the ClustAll algorithm is executed, ClustAll output can be explored.
3.1.2.0.3 plotJACCARD
This function plots the correlation matrix heatmap showing the Jaccard
Distance between robust stratifications.
The usage and arguments
are the following:
Object - ClustAllObject-class object. paint - Logical value. If
TRUE, group of similar stratifications are highlighted within a red
square. stratification_similarity - Numeric value. The minimum Jaccard
Distance value to consider a of pair stratifications similar. Default is
0.7.
plotJACCARD(Object = obj_noNA1, stratification_similarity = 0.7)
plotJACCARD(Object = obj_noNA1, stratification_similarity = 0.6)
plotJACCARD(Object = obj_noNA1, stratification_similarity = 0.9)
plotJACCARD(Object = obj_noNA1, stratification_similarity = 0.88)
plotJACCARD(Object = obj_noNA1simplify, stratification_similarity = 0.88)
3.1.2.0.4 resStratification
This function returns the stratification representatives by filtering
those stratifications that does not contain clusters with a minimum
percentage of the population. It can return all the robust
stratifications or just one representative each group of robust
stratifications.
The usage and arguments are the following:
Object - ClustAllObject-class object. population - Numeric value.
Minimum percentage of the total population that a cluster in a
stratification must have to be considered as representative. Default is
0.05 (5%). stratification_similarity - Numerical value. The minimum
Jaccard Distance value to consider a pair of stratifications similar.
Default is 0.7.
all - Logical value. If TRUE, it returns all the similar
representative stratifications. If FALSE, only the centroid
stratification for each group of similar stratifications is returned.
resStratification(Object = obj_noNA1, population = 0.05,
stratification_similarity = 0.88, all = FALSE)
#> $cuts_a_9
#> $cuts_a_9[[1]]
#>
#> 1 2
#> 193 376
#>
#>
#> $cuts_c_3
#> $cuts_c_3[[1]]
#>
#> 1 2
#> 199 3703.1.2.0.5 plotSANKEY
This function plots the Sankey diagram of two selected clusters in order
to compare them.
The usage and arguments are the following:
Object - ClustAllObject-class object. clusters - Character vector
with the names of a pair of stratifications. Check resStratification to
obtain all the stratification names.
plotSANKEY(Object = obj_noNA1, clusters = c("cuts_c_3","cuts_a_9"),
validationData = FALSE)plotSANKEY(Object = obj_noNA1, clusters = c("cuts_c_3","cuts_b_13"),
validationData = FALSE)plotSANKEY(Object = obj_noNA1, clusters = "cuts_c_3", validationData = TRUE)plotSANKEY(Object = obj_noNA1, clusters = "cuts_b_13", validationData = TRUE)3.1.2.0.6 cluster2data
This function returns a dataframe with the original dataset together
with the selected ClustAll stratification(s) included as variables.
The usage and arguments are the following:
Object - ClustAllObject-class object. clusterName - Character vector
with one or more stratification names.
df <- cluster2data(Object = obj_noNA1,
clusterName = c("cuts_c_3","cuts_a_9","cuts_b_13"))3.1.2.0.7 Validation
This function aims to validate the results obtained with the (multiple) robust stratification(s) with the original data labelling if it was present in the original dataset.
# STRATIFICATION 1
validateStratification(obj_noNA1, "cuts_a_9")
#> sensitivity specifity
#> 0.8396226 0.9579832# STRATIFICATION 2
validateStratification(obj_noNA1, "cuts_b_13")
#> sensitivity specifity
#> 0.8160377 0.9243697# STRATIFICATION 3
validateStratification(obj_noNA1, "cuts_c_3")
#> sensitivity specifity
#> 0.8867925 0.96918773.1.3 b) Workflow with a dataset with missing values and imputation performed within ClustAll framework
The same procedure where the dataset contains missing values.
3.1.3.0.1 createClustAll and perform imputation
data("BreastCancerWisconsinMISSING", package = "ClustAll")
data_use_NA <- wdbcNA
colSums(is.na(data_use_NA)) # dataset present NAs
#> Diagnosis radius1 texture1 perimeter1
#> 0 17 0 0
#> area1 smoothness1 compactness1 concavity1
#> 0 0 0 34
#> concave_points1 symmetry1 fractal_dimension1 radius2
#> 13 0 0 0
#> texture2 perimeter2 area2 smoothness2
#> 40 0 0 0
#> compactness2 concavity2 concave_points2 symmetry2
#> 30 0 20 34
#> fractal_dimension2 radius3 texture3 perimeter3
#> 0 48 56 0
#> area3 smoothness3 compactness3 concavity3
#> 0 22 0 0
#> concave_points3 symmetry3 fractal_dimension3
#> 0 0 52
obj_NA <- createClustAll(data_use_NA, nImputation = 5,
colValidation = "Diagnosis")
#> Before continuing, check that the transformation has been processed correctly.
#> Running default multiple imputation method.
#> For more information check mice package.
#>
#> iter imp variable
#> 1 1 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 1 2 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 1 3 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 1 4 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 1 5 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 2 1 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 2 2 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 2 3 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 2 4 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 2 5 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 3 1 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 3 2 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 3 3 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 3 4 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 3 5 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 4 1 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 4 2 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 4 3 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 4 4 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 4 5 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 5 1 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 5 2 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 5 3 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 5 4 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> 5 5 radius1 concavity1 concave_points1 texture2 compactness2 concave_points2 symmetry2 radius3 texture3 smoothness3 fractal_dimension3
#> Warning: Number of logged events: 225
#>
#> ClustALL object created successfully. You can use runClustAll.
# The rest of the pipeline follows as a)3.1.3.0.2 runClustAll method
obj_NA1 <- runClustAll(obj_NA, threads = 8)
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!3.1.3.0.3 plotJACCARD
plotJACCARD(Object = obj_NA1, stratification_similarity = 0.9)
3.1.3.0.4 resStratification
resStratification(Object = obj_NA1, population = 0.05,
stratification_similarity = 0.88, all = FALSE)
#> $cuts_a_3
#> $cuts_a_3[[1]]
#>
#> 1 2
#> 196 3733.1.3.0.5 cluster2data
df_NA <- cluster2data(Object = obj_NA1, clusterName = c("cuts_a_4"))3.1.4 c) Workflow with a dataset with missing values and imputation performed out of ClustAll framework
The same procedure where the dataset has been previously imputed.
3.1.4.0.1 Perform imputation and store in an mids object. In this case, mice package is used.
library(mice)
#>
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#>
#> filter
#> The following objects are masked from 'package:base':
#>
#> cbind, rbind
data("BreastCancerWisconsinMISSING", package = "ClustAll") # load example data
data_use_NA <- wdbcNA
str(data_use_NA)
#> 'data.frame': 569 obs. of 31 variables:
#> $ Diagnosis : Factor w/ 2 levels "B","M": 2 2 2 2 2 2 2 2 2 2 ...
#> $ radius1 : num 18 20.6 19.7 11.4 20.3 ...
#> $ texture1 : num 10.4 17.8 21.2 20.4 14.3 ...
#> $ perimeter1 : num 122.8 132.9 130 77.6 135.1 ...
#> $ area1 : num 1001 1326 1203 386 1297 ...
#> $ smoothness1 : num 0.1184 0.0847 0.1096 0.1425 0.1003 ...
#> $ compactness1 : num 0.2776 0.0786 0.1599 0.2839 0.1328 ...
#> $ concavity1 : num 0.3001 0.0869 0.1974 0.2414 0.198 ...
#> $ concave_points1 : num 0.1471 0.0702 0.1279 0.1052 0.1043 ...
#> $ symmetry1 : num 0.242 0.181 0.207 0.26 0.181 ...
#> $ fractal_dimension1: num 0.0787 0.0567 0.06 0.0974 0.0588 ...
#> $ radius2 : num 1.095 0.543 0.746 0.496 0.757 ...
#> $ texture2 : num 0.905 0.734 0.787 1.156 NA ...
#> $ perimeter2 : num 8.59 3.4 4.58 3.44 5.44 ...
#> $ area2 : num 153.4 74.1 94 27.2 94.4 ...
#> $ smoothness2 : num 0.0064 0.00522 0.00615 0.00911 0.01149 ...
#> $ compactness2 : num 0.049 0.0131 0.0401 0.0746 0.0246 ...
#> $ concavity2 : num 0.0537 0.0186 0.0383 0.0566 0.0569 ...
#> $ concave_points2 : num 0.0159 0.0134 0.0206 0.0187 0.0188 ...
#> $ symmetry2 : num 0.03 0.0139 0.0225 0.0596 0.0176 ...
#> $ fractal_dimension2: num 0.00619 0.00353 0.00457 0.00921 0.00511 ...
#> $ radius3 : num 25.4 25 23.6 14.9 22.5 ...
#> $ texture3 : num NA 23.4 25.5 26.5 16.7 ...
#> $ perimeter3 : num 184.6 158.8 152.5 98.9 152.2 ...
#> $ area3 : num 2019 1956 1709 568 1575 ...
#> $ smoothness3 : num 0.162 0.124 0.144 0.21 0.137 ...
#> $ compactness3 : num 0.666 0.187 0.424 0.866 0.205 ...
#> $ concavity3 : num 0.712 0.242 0.45 0.687 0.4 ...
#> $ concave_points3 : num 0.265 0.186 0.243 0.258 0.163 ...
#> $ symmetry3 : num 0.46 0.275 0.361 0.664 0.236 ...
#> $ fractal_dimension3: num 0.1189 0.089 0.0876 0.173 0.0768 ...
imp_data_use <- mice(data_use_NA[-1], m=5, maxit = 5,seed=1234, print=FALSE)
#> Warning: Number of logged events: 2253.1.4.0.2 createClustAll object
# dataImputed contains the mids object with the imputed
obj_imp <- createClustAll(data=data_use_NA, dataImputed = imp_data_use,
colValidation = "Diagnosis")
#> Before continuing, check that the transformation has been processed correctly.
#>
#> ClustALL object created successfully. You can use runClustAll.
# The rest of the pipeline follows as a)3.1.4.0.3 runClustAll method
obj_imp1 <- runClustAll(obj_imp, threads = 8)
#> ______ __ __ ___ __ __
#> / ____// /__ __ _____ / /_ / | / / / /
#> / / / // / / // ___// __// /| | / / / /
#> / /___ / // /_/ /(__ )/ /_ / ___ | / /___ / /___
#> /_____//_/ |__,_//____/ |__//_/ |_|/_____//_____/
#> Running Data Complexity Reduction and Stratification Process.
#> This step may take some time...
#>
#>
#> Filtering non-robust statifications...
#>
#> ClustAll pipeline finished successfully!3.1.4.0.4 plotJACCARD
plotJACCARD(Object = obj_imp1, stratification_similarity = 0.9)
3.1.4.0.5 resStratification
resStratification(Object = obj_imp1, population = 0.05,
stratification_similarity = 0.9, all = FALSE)
#> $cuts_a_1
#> $cuts_a_1[[1]]
#>
#> 1 2
#> 187 382
#>
#>
#> $cuts_a_4
#> $cuts_a_4[[1]]
#>
#> 1 2
#> 194 3753.1.4.0.6 cluster2data
df_imp <- cluster2data(Object = obj_NA1,
clusterName = c("cuts_a_1", "cuts_a_16"))
validateStratification(obj_noNA1, "cuts_a_1")
#> sensitivity specifity
#> 0.8254717 0.9607843
validateStratification(obj_noNA1, "cuts_a_16")
#> sensitivity specifity
#> 0.8349057 0.9551821Session Info
sessionInfo()
#> R Under development (unstable) (2024-01-16 r85808)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] mice_3.16.0 ClustAll_0.99.1 BiocStyle_2.31.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_1.8.8 shape_1.4.6
#> [4] magrittr_2.0.3 magick_2.8.2 TH.data_1.1-2
#> [7] estimability_1.4.1 modeltools_0.2-23 jomo_2.7-6
#> [10] nloptr_2.0.3 rmarkdown_2.25 GlobalOptions_0.1.2
#> [13] vctrs_0.6.5 Cairo_1.6-2 minqa_1.2.6
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