Meta Clustering with Similarity Network Fusion
Add columns to a data frame
Add rows to a settings_df
Heatmap of pairwise adjusted rand indices between solutions
Alluvial plot of patients across cluster counts and important features
Sort data frames in a data list by their unique ID values
Convert an object to an ARI matrix
Convert an object to a data list
Convert an object to a settings data frame
Convert an object to a similarity matrix list
Convert an object to a snf config
Convert an object to a weights matrix
Coerce a data_list class object into a data.frame class object
Coerce a ext_solutions_df class object into a data.frame class obj...
Coerce a settings_df class object into a data.frame class object
Coerce a settings_df class object into a data.frame class object
Coerce a solutions_df class object into a data.frame class object
Coerce a t_ext_solutions_df class object into a data.frame class o...
Coerce a t_solutions_df class object into a data.frame class objec...
Coerce a weights_matrix class object into a data.frame class objec...
Coerce a clust_fns_list class object into a list class object
Coerce a data_list class object into a list class object
Coerce a dist_fns_list class object into a list class object
Coerce a sim_mats_list class object into a list class object
Coerce a snf_config class object into a list class object
Coerce a ari_matrix class object into a matrix class object
Coerce a weights_matrix class object into a matrix class object
Collapse a data frame and/or a data list into a single data frame
Heatmap of pairwise associations between features
Automatically plot features across clusters
Bar plot separating a feature by cluster
Generate closure function to run batch_snf in an apply-friendly format
Run SNF clustering pipeline on a list of subsampled data lists
Run variations of SNF
Construct an ARI matrix storing inter-solution similarities
Calculate p-values for all pairwise associations of features in a data...
Calculate p-values based on feature vectors and their types
Calculate feature NMIs for a data list and a solutions data frame
Calculate co-clustering data
Helper function for generating categorical colour palette
Place significance stars on ComplexHeatmap cells
Convert character-type columns of a data frame to factor-type
Check if functions in a distance metrics list-like have valid argument...
Check if items of a clustering functions list-like object are function...
Check if clustering functions list-like object has named algorithms
Check if names in a clustering functions list-like object are unique
Check if settings_df exceeds bounds of clust_fns_list
Check if settings_df exceeds bounds of dist_fns_list
Check if settings_df and weights_matrix have same number of rows
Helper function to stop annotation building when no data was provided
Check if functions in a distance metrics list-like have valid argument...
Check if functions in a distance metrics list-like have names
Check if items of a distance metrics list-like object have valid names
Check if items of a distance metrics list-like object are functions
Check if names in a distance metrics list-like object are unique
Check if data list contains any duplicate names
Check if data list contains any duplicate features
Error if empty input provided during data list initialization
Error if data list-like list doesn't have only 4-item nested lists
Error if data list-like structure isn't a list
Check if UID columns in a nested list have valid structure for a data ...
Check valid item names for a data list-like list
Error if data list-like structure has invalid feature types
Check if UID columns in a nested list have valid structure for a data ...
Check for ComplexHeatmap and circlize dependencies
Check if settings data frame inherits class data.frame
Check if settings data frame inherits class data.frame
Check if settings data frame is numeric
Check validity of similarity matrices
Check if max K exceeds the number of observations
Check if SNF config has valid structure
Chi-squared test p-value (generic)
Build a clustering algorithms list
Built-in clustering algorithms
Density plot of co-clustering stability across subsampled data
Heatmap of observation co-clustering across resampled data
Co-clustering coverage check
Convert a data list into a data frame
Return a colour ramp for a given vector
Convert unique identifiers of data list to "uid"
Build a data_list class object
Internal function for estimate_nclust_given_graph
Internal function for estimate_nclust_given_graph
Build a distance metrics list
Built-in distance functions
Variable-level summary of a data list
Apply-like function for data list objects
Make the uid UID columns of a data list first
Pull domains from a data list
Function to extend dplyr to extended solutions data frame objects
Function to extend dplyr to solutions data frame objects
Helper function to remove columns from a data frame
Execute inclusion
Ensure the data item of each component is a data.frame class object
Manhattan plot of feature-cluster association p-values
Estimate number of clusters for a similarity matrix
Constructor for ext_solutions_df class object
Extend a solutions data frame to include outcome evaluations
Return character vector of features stored in an object
Fisher exact test p-value
Generate annotations list
Generate a clustering algorithms list
Generate a list of distance metrics
Build a settings data frame
Extract cluster membership information from one solutions data frame r...
Extract cluster membership information from a sol_df
Extract cluster membership vector from one solutions data frame row
Pull complete-data UIDs from a list of data frames
Calculate distance matrices
Extract UIDs from a data list
Return the row or column ordering present in a heatmap
Return the hierarchical clustering order of a matrix
Get mean p-value
Get minimum p-value
Get p-values from an extended solutions data frame
Extract representative solutions from a matrix of ARIs
Helper function to drop columns from a data frame by grepl search
Helper function to pick columns from a data frame by grepl search
Test if the object is a data list
Jitter plot separating a feature by cluster
Assign meta cluster labels to rows of a solutions data frame or extend...
Label propagation
Label propagate cluster solutions to non-clustered observations
Convert a vector of partition indices into meta cluster labels
Linearly correct data list by features with unwanted signal
Linear model p-value (generic)
Manhattan plot of feature-meta cluster association p-values
Merge list of data frames into a single data frame
Merge clust_fns_list objects
Merge observations between two compatible data lists
Merge dist_fns_list objects
Merge ext_solutions_df objects
Merge settings_df objects
Merge sim_mats_list objects
Merge method for SNF config objects
Merge solutions_df objects
Merge t_ext_solutions_df objects
Merge t_solutions_df objects
Merge weights_matrix objects
Helper function for raising alerts
Helper function for defunct function errors
Helper function for deprecated function warnings
Helper function for raising errors
Helper function for raising warnings
metasnf: Meta Clustering with Similarity Network Fusion
Extract number of features stored in an object
Extract number of observations stored in an object
Constructor for ari_matrix class object
Constructor for clust_fns_list class object
Constructor for data_list class object
Constructor for dist_fns_list class object
Constructor for ext_solutions_df class object
Constructor for settings_df class object
Constructor for similarity_matrix_list class object
Constructor for snf_config class object
Constructor for solutions_df class object
Constructor for weights_matrix class object
Helper function for creating what hidden ft/obs/sols message
Convert columns of a data frame to numeric type (if possible)
Ordinal regression p-value
Parallel processing form of batch_snf
Helper function to pick columns from a data frame
Helper function to pluralize a string
Heatmap of pairwise adjusted rand indices between solutions
Plot of feature values in a data list
Plot of cluster assignments in an extended solutions data frame
Heatmap for visualizing an SNF config
Plot of cluster assignments in a solutions data frame
Add "uid_" prefix to all UID values in uid column
Helper function for outputting tip on changing rows printed
Helper function for transposing solutions_df message
Print method for class ari_matrix
Print method for class clust_fns_list
Print method for class data_list
Print method for class dist_fns_list
Print method for class ext_solutions_df
Print method for class settings_df
Print method for class sim_mats_list
Print method for class snf_config
Print method for class solutions_df
Print method for class t_ext_solutions_df
Print method for class t_solutions_df
Print method for class weights_matrix
Heatmap of p-values
Quality metrics
Generate random removal sequence
Row-binding of solutions data frame class objects
Row-binding of solutions data frame class objects
Row-binding of t_solutions_df class objects
Row-bind weights matrices
Remove observations with incomplete data from a data list-like list ob...
Rename features in a data list
Reorder the uids in a data list
Helper resampling function found in ?sample
Run SNF
Save a heatmap object to a file
Adjust the diagonals of a matrix
Build a settings data frame
Launch a shiny app to identify meta cluster boundaries
Create or extract a sim_mats_list class object
Plot heatmap of similarity matrix
Generate a complete path and filename to store an similarity matrix
Squared (including weights) Euclidean distance
Define configuration for generating a set of SNF-based cluster solutio...
SNF schemes
Helper function for using the correct SNF scheme
Helper function for organizing solutions df-like column order
Constructor for solutions_df class object
Helper function to determine which row and columns to split on
Structure of a ari_matrix object
Structure of a clust_fns_list object
Structure of a data_list object
Structure of a dist_fns_list object
Structure of a ext_solutions_df object
Structure of a settings_df object
Structure of a sim_mats_list object
Structure of a snf_config object
Structure of a solutions_df object
Structure of a t_ext_solutions_df object
Structure of a t_solutions_df object
Structure of a weights_matrix object
Create subsamples of a data list
Calculate pairwise adjusted Rand indices across subsamples of data
Summarize a clust_fns_list object
Summarize a distance functions list
Summarize a data list
Summarize p-value columns of an extended solutions data frame
Pull features used to calculate summary p-values from an object
Summary method for class ari_matrix
Summary method for class clust_fns_list
Summary method for class data_list
Summary method for class dist_fns_list
Summary method for class ext_solutions_df
Summary method for class settings_df
Summary method for class sim_mats_list
Summary method for class snf_config
Summary method for class solutions_df
Summary method for class t_ext_solutions_df
Summary method for class t_solutions_df
Summary method for class weights_matrix
Training and testing split
Pull UIDs from an object
Validator for ari_matrix class object
Validator for clust_fns_list class object
Validator for data_list class object
Validator for dist_fns_list class object
Validator for ext_solutions_df class object
Validator for settings_df class object
Validator for similarity_matrix_list class object
Validator for snf_config class object
Validator for solutions_df class object
Validator for weights_matrix class object
Manhattan plot of feature-feature association p-values
Generate a matrix to store feature weights
Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.
Useful links