Package index • metasnf

All functions

abcd_anxiety: Mock ABCD anxiety data

abcd_colour: Mock ABCD "colour" data

abcd_cort_sa: Mock ABCD cortical surface area data

abcd_cort_t: Mock ABCD cortical thickness data

abcd_depress: Mock ABCD depression data

abcd_h_income: Mock ABCD income data

abcd_income: Mock ABCD income data

abcd_pubertal: Mock ABCD pubertal status data

abcd_subc_v: Mock ABCD subcortical volumes data

add_columns(): Add columns to a dataframe

add_settings_matrix_rows(): Add settings matrix rows

adjusted_rand_index_heatmap(): Heatmap of pairwise adjusted rand indices between solutions

age_df: Mock age data

alluvial_cluster_plot(): Alluvial plot of patients across cluster counts and important features

anxiety: Mock ABCD anxiety data

arrange_dl(): Given a data_list object, sort data elements by subjectkey

assemble_data(): Collapse a dataframe and/or a data_list into a single dataframe

assoc_pval_heatmap(): Heatmap of pairwise associations between features

auto_plot(): Automatically plot features across clusters

bar_plot(): Bar plot separating a feature by cluster

batch_nmi(): Calculate feature NMIs for a data_list and a derived solutions_matrix

batch_row_closure(): Generate closure function to run batch_snf in an apply-friendly format

batch_snf(): Run variations of SNF.

batch_snf_subsamples(): Run SNF clustering pipeline on a list of subsampled data lists.

calc_aris(): Meta-cluster calculations

calc_assoc_pval(): Calculate p-values based on feature vectors and their types

calc_assoc_pval_matrix(): Calculate p-values for all pairwise associations of features in a data_list

calculate_coclustering(): Calculate coclustering data.

calculate_db_indices(): Calculate Davies-Bouldin indices

calculate_dunn_indices(): Calculate Dunn indices

calculate_silhouettes(): Calculate silhouette scores

cancer_diagnosis_df: Mock diagnosis data

cell_significance_fn(): Place significance stars on ComplexHeatmap cells.

char_to_fac(): Convert character-type columns of a dataframe to factor-type

check_dataless_annotations(): Helper function to stop annotation building when no data was provided

check_hm_dependencies(): Check for ComplexHeatmap and circlize dependencies

check_similarity_matrices(): Check validity of similarity matrices

chi_squared_pval(): Chi-squared test p-value (generic)

cocluster_density(): Density plot coclustering stability across subsampled data.

cocluster_heatmap(): Heatmap of observation co-clustering across resampled data.

coclustering_coverage_check(): Coclustering coverage check

collapse_dl(): Collapse a data_list into a single dataframe

colour_scale(): Return a colour ramp for a given vector

convert_uids(): Convert unique identifiers of data_list to 'subjectkey'

cort_sa: Mock ABCD cortical surface area data

cort_t: Mock ABCD cortical thickness data

depress: Mock ABCD depression data

diagnosis_df: Mock diagnosis data

discretisation(): Internal function for estimate_nclust_given_graph

discretisation_evec_data(): Internal function for estimate_nclust_given_graph

dl_has_duplicates(): Check if data list contains any duplicate features

dl_uid_first_col(): Make the subjectkey UID columns of a data_list first

dl_variable_summary(): Variable-level summary of a data_list

domain_merge(): SNF scheme: Domain merge

domains(): Domains

drop_inputs(): Execute inclusion

esm_manhattan_plot(): Manhattan plot of feature-cluster association p-values

estimate_nclust_given_graph(): Estimate number of clusters for a similarity matrix

euclidean_distance(): Distance metric: Euclidean distance

expression_df: Modification of SNFtool mock dataframe "Data1"

extend_solutions(): Extend an solutions matrix to include outcome evaluations

fav_colour: Mock ABCD "colour" data

fisher_exact_pval(): Fisher exact test p-value

gender_df: Mock gender data

generate_annotations_list(): Generate annotations list

generate_clust_algs_list(): Generate a list of custom clustering algorithms

generate_data_list(): Generate a data_list

generate_distance_metrics_list(): Generate a list of distance metrics

generate_settings_matrix(): Build a settings matrix

generate_weights_matrix(): Generate a matrix to store feature weights

get_cluster_df(): Extract cluster membership information from one solutions matrix row

get_cluster_solutions(): Extract cluster membership information from a solutions_matrix

get_clusters(): Extract cluster membership vector from one solutions matrix row

get_complete_uids(): Pull complete-data UIDs from a list of dataframes

get_dist_matrix(): Calculate distance matrices

get_dl_subjects(): Extract subjects from a data_list

get_heatmap_order(): Return the row or column ordering present in a heatmap

get_matrix_order(): Return the hierarchical clustering order of a matrix

get_mean_pval(): Get mean p-value

get_min_pval(): Get minimum p-value

get_pvals(): Get p-values from an extended solutions matrix

get_representative_solutions(): Extract representative solutions from a matrix of ARIs

gower_distance(): Distance metric: Gower distance

hamming_distance(): Distance metric: Hamming distance

income: Mock ABCD income data

individual(): SNF Scheme: Individual

jitter_plot(): Jitter plot separating a feature by cluster

label_prop(): Label propagation

label_splits(): Convert a vector of partition indices into meta cluster labels

linear_adjust(): Linearly correct data_list by features with unwanted signal

linear_model_pval(): Linear model p-value (generic)

list_remove(): Remove items from a data_list

lp_solutions_matrix(): Label propagate cluster solutions to unclustered subjects

mc_manhattan_plot(): Manhattan plot of feature-meta cluster associaiton p-values

merge_data_lists(): Horizontally merge compatible data lists

merge_df_list(): Merge list of dataframes

methylation_df: Modification of SNFtool mock dataframe "Data2"

no_subs(): Select all columns of a dataframe not starting with the 'subject_' prefix.

numcol_to_numeric(): Convert dataframe columns to numeric type

ord_reg_pval(): Ordinal regression p-value

parallel_batch_snf(): Parallel processing form of batch_snf

prefix_dl_sk(): Add "subject_" prefix to all UID values in subjectkey column

pubertal: Mock ABCD pubertal status data

pval_heatmap(): Heatmap of p-values

random_removal(): Generate random removal sequence

reduce_dl_to_common(): Reduce data_list to common subjects

remove_dl_na(): Remove NAs from a data_list object

rename_dl(): Rename features in a data_list

reorder_dl_subs(): Reorder the subjects in a data_list

resample(): Helper resample function found in ?sample

save_heatmap(): Save a heatmap object to a file

scale_diagonals(): Adjust the diagonals of a matrix

settings_matrix_heatmap(): Heatmap for visualizing a settings matrix

sew_euclidean_distance(): Squared (excluding weights) Euclidean distance

shiny_annotator(): Launch shiny app to identify meta cluster boundaries

similarity_matrix_heatmap(): Plot heatmap of similarity matrix

similarity_matrix_path(): Generate a complete path and filename to store an similarity matrix

siw_euclidean_distance(): Squared (including weights) Euclidean distance

sn_euclidean_distance(): Distance metric: Standard normalization then Euclidean

snf_step(): Convert a data list to a similarity matrix through a variety of SNF schemes

spectral_eigen(): Clustering algorithm: Spectral clustering with eigen-gap heuristic

spectral_eigen_classic(): Clustering algorithm: Spectral clustering with eigen-gap heuristic

spectral_eight(): Clustering algorithm: Spectral clustering for a eight cluster solution

spectral_five(): Clustering algorithm: Spectral clustering for a five cluster solution

spectral_four(): Clustering algorithm: Spectral clustering for a four cluster solution

spectral_nine(): Clustering algorithm: Spectral clustering for a nine cluster solution

spectral_rot(): Clustering algorithm: Spectral clustering with rotation cost heuristic

spectral_rot_classic(): Clustering algorithm: Spectral clustering with rotation cost heuristic

spectral_seven(): Clustering algorithm: Spectral clustering for a seven cluster solution

spectral_six(): Clustering algorithm: Spectral clustering for a six cluster solution

spectral_ten(): Clustering algorithm: Spectral clustering for a ten cluster solution

spectral_three(): Clustering algorithm: Spectral clustering for a three cluster solution

spectral_two(): Clustering algorithm: Spectral clustering for a two cluster solution

split_parser(): Helper function to determine which row and columns to split on

subc_v: Mock ABCD subcortical volumes data

subs(): Select all columns of a dataframe starting with a given string prefix.

subsample_data_list(): Create subsamples of a data_list

subsample_pairwise_aris(): Calculate pairwise adjusted Rand indices across subsamples of data

summarize_clust_algs_list(): Summarize a clust_algs_list object

summarize_dl(): Summarize a data list

summarize_dml(): Summarize metrics contained in a distance_metrics_list

summarize_pvals(): Summarize p-value columns of an extended solutions matrix

train_test_assign(): Training and testing split

two_step_merge(): Two step SNF

var_manhattan_plot(): Manhattan plot of feature-feature associaiton p-values