Package: ClustAssess 0.3.1

Arash Shahsavari

ClustAssess: Tools for Assessing Clustering

A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

Authors:Arash Shahsavari [aut, cre], Andi Munteanu [aut], Irina Mohorianu [aut]

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ClustAssess.pdf |ClustAssess.html✨
ClustAssess/json (API)

# Install 'ClustAssess' in R:
install.packages('ClustAssess', repos = c('https://core-bioinformatics.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/core-bioinformatics/clustassess/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

bioinformaticsclusteringgenomicsmachine-learningparameter-optimizationrobustnesssingle-cellunsupervised-learning

27 exports 21 stars 2.11 score 57 dependencies 9 scripts 310 downloads

Last updated 2 years agofrom:299c3b5ffd. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 11 2024
R-4.5-win-x86_64OKSep 11 2024
R-4.5-linux-x86_64OKSep 11 2024
R-4.4-win-x86_64OKSep 11 2024
R-4.4-mac-x86_64OKSep 11 2024
R-4.4-mac-aarch64OKSep 11 2024
R-4.3-win-x86_64OKSep 11 2024
R-4.3-mac-x86_64OKSep 11 2024
R-4.3-mac-aarch64OKSep 11 2024

Exports:%>%consensus_clusterelement_agreementelement_consistencyelement_simelement_sim_elscoreelement_sim_matrixget_clustering_differenceget_feature_stabilityget_nn_conn_compsget_nn_importanceget_resolution_importancemarker_overlapmerge_partitionspac_convergencepac_landscapeplot_clustering_difference_boxplotplot_clustering_difference_facetplot_connected_comps_evolutionplot_feature_stability_boxplotplot_feature_stability_ecs_facetplot_feature_stability_ecs_incrementalplot_feature_stability_mb_facetplot_k_n_partitionsplot_k_resolution_correspplot_n_neigh_ecsplot_n_neigh_k_correspondence

Dependencies:BHclicodetoolscolorspacecpp11crayondoParalleldplyrdqrngfansifarverfastclusterFNNforeachgenericsggplot2gluegtablehmsigraphirlbaisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrprettyunitsprogressR6RColorBrewerRcppRcppAnnoyRcppEigenRcppProgressreshape2rlangRSpectrascalessitmostringistringrtibbletidyselectutf8uwotvctrsviridisLitewithr

Comparing soft and hierarchical clusterings with element-centric similarity

Rendered fromcomparing-soft-and-hierarchical.Rmdusingknitr::rmarkdownon Sep 11 2024.

Last update: 2021-11-25
Started: 2021-03-18

Evaluating single-cell clustering with ClustAssess

Rendered fromClustAssess.Rmdusingknitr::rmarkdownon Sep 11 2024.

Last update: 2022-02-07
Started: 2020-12-17

Readme and manuals

Help Manual

Help pageTopics
Consensus Clustering and Proportion of Ambiguously Clustered Pairsconsensus_cluster
Element-Wise Average Agreement Between a Set of Clusteringselement_agreement
Element-Wise Consistency Between a Set of Clusteringselement_consistency
The Element-Centric Clustering Similarityelement_sim
The Element-Centric Clustering Similarity for each Elementelement_sim_elscore
Pairwise Comparison of Clusteringselement_sim_matrix
Graph Clustering Method Stabilityget_clustering_difference
Evaluate Feature Set Stabilityget_feature_stability
Relationship Between Nearest Neighbors and Connected Componentsget_nn_conn_comps
Assess Graph Building Parametersget_nn_importance
Evaluate Stability Across Resolution, Number of Neighbors, and Graph Typeget_resolution_importance
Cell-Wise Marker Gene Overlapmarker_overlap
Merge Partitionsmerge_partitions
PAC Convergence Plotpac_convergence
PAC Landscape Plotpac_landscape
Clustering Method Stability Boxplotplot_clustering_difference_boxplot
Clustering Method Stability Facet Plotplot_clustering_difference_facet
Relationship Between Number of Nearest Neighbors and Graph Connectivityplot_connected_comps_evolution
Feature Stability Boxplotplot_feature_stability_boxplot
Feature Stability - EC Consistency Facet Plotplot_feature_stability_ecs_facet
Feature Stability Incremental Boxplotplot_feature_stability_ecs_incremental
Feature Stability - Cluster Membership Facet Plotplot_feature_stability_mb_facet
Relationship Between the Number of Clusters and the Number of Unique Partitionsplot_k_n_partitions
Correspondence Between Resolution and the Number of Clustersplot_k_resolution_corresp
Graph construction parameters - ECC facetplot_n_neigh_ecs
Relationship Between Number of Nearest Neighbors and Number of Clustersplot_n_neigh_k_correspondence