Package index • ClustAssess

Element-Centric Scores

element_agreement(): Element-Wise Average Agreement Between a Set of Clusterings

element_consistency(): Element-Wise Consistency Between a Set of Clusterings

element_sim(): The Element-Centric Clustering Similarity

element_sim_elscore(): The Element-Centric Clustering Similarity for each Element

element_sim_matrix(): Pairwise Comparison of Clusterings

marker_overlap(): Cell-Wise Marker Gene Overlap

merge_partitions(): Merge Partitions

merge_resolutions(): Merge Partitions from different Resolutions

weighted_element_consistency(): Weighted Element-Centric Consistency

Clustering Assessment Pipeline

assess_clustering_stability(): Assessment of Stability for Graph Clustering

assess_feature_stability(): Assess the stability for configurations of feature types and sizes

assess_nn_stability(): Assess the stability for Graph Building Parameters

plot_clustering_difference_facet(): Clustering Method Stability Facet Plot

plot_clustering_overall_stability(): Clustering Method Overall Stability Boxplot

plot_clustering_per_value_stability(): Clustering Method per value Stability Boxplot

plot_connected_comps_evolution(): Relationship Between Number of Nearest Neighbours and Graph Connectivity

plot_feature_overall_stability_boxplot(): Overall Feature Stability Boxplot

plot_feature_overall_stability_incremental(): Overall Feature Stability Incremental Boxplot

plot_feature_per_resolution_stability_boxplot(): Per resolution Feature Stability Boxplot

plot_feature_per_resolution_stability_incremental(): Per resolution - Feature Stability Incremental Boxplot

plot_feature_stability_ecs_facet(): Feature Stability - EC Consistency Facet Plot

plot_feature_stability_mb_facet(): Feature Stability - Cluster Membership Facet Plot

plot_k_n_partitions(): Relationship Between the Number of Clusters and the Number of Unique Partitions

plot_k_resolution_corresp(): Correspondence Between Resolution and the Number of Clusters

plot_n_neigh_ecs(): Graph construction parameters - ECC facet

plot_n_neigh_k_correspondence(): Relationship Between Number of Nearest Neighbours and Number of Clusters

getNNmatrix(): Computes the NN adjacency matrix given the neighbours

get_clusters_from_clustassess_object(): Extract config-specific clusters from a ClustAssess object

get_highest_prune_param(): Calculate the highest pruning parameter for the SNN graph given NN matrix

get_highest_prune_param_embedding(): Calculate the highest pruning parameter for the SNN graph given Embedding

get_nn_conn_comps(): Relationship Between Nearest Neighbours and Connected Components

automatic_stability_assessment(): Assessment of Stability for Graph Clustering

choose_stable_clusters(): Choose stable clusters based on ECC and frequency

Shiny App Components

ui_comparisons(): UI - Comparison module

ui_dimensionality_reduction(): UI - Dimensionality reduction module

ui_graph_clustering(): UI - Graph clustering module

ui_graph_construction(): UI - Graph construction module

ui_landing_page(): UI - Landing page module

ui_sandbox(): UI - Sandbox module

server_comparisons(): Server - Comparison module

server_dimensionality_reduction(): Server - Dimensionality reduction module

server_graph_clustering(): Server - Graph clustering module

server_graph_construction(): Server - Graph construction module

server_landing_page(): Server - Landing page module

server_sandbox(): Server - Sandbox module

write_objects(): Write the objects for the ClustAssess ShinyApp

write_shiny_app(): Create the ClustAssess ShinyApp

add_metadata(): Add metadata to ClustAssess ShinyApp

calculate_markers(): Calculate markers

calculate_markers_shiny(): Calculate markers - Shiny

Convert to other packages

create_monocle_default(): Create monocle object

create_monocle_from_clustassess(): Create monocle object from a ClustAssess object

create_monocle_from_clustassess_app(): Create monocle object from a ClustAssess shiny app

create_seurat_object_default(): Create Seurat object

create_seurat_object_from_clustassess_app(): Create Seurat object from a ClustAssess shiny app

PAC

consensus_cluster(): Consensus Clustering and Proportion of Ambiguously Clustered Pairs

pac_convergence(): PAC Convergence Plot

pac_landscape(): PAC Landscape Plot