Package 'metasnf'

Description

A randomly shuffled and anonymized copy of anxiety data from the NIMH Data archive. The original file used was pdem02.txt. The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_cbcl_anxiety.

Usage

abcd_anxiety

Format

abcd_anxiety

A data frame with 275 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

cbcl_anxiety_r

Ordinal value of impairment on CBCL anxiety, either 0 (no impairment), 1 (borderline clinical), or 2 (clinically impaired)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of depression data from the NIMH Data archive. The original file used was pdem02.txt. The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_cbcl_depress. The data was transformed into categorical colour values to demonstrate the Chi-squared test capabilities of extend_solutions.

Usage

abcd_colour

Format

abcd_colour

A data frame with 275 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

colour

Categorical transformation of cbcl_depress.

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of cortical surface area data from the NIMH Data archive. The original file used was mrisdp10201.txt The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_cort_t.

Usage

abcd_cort_sa

Format

abcd_cort_sa

A data frame with 188 rows and 152 columns:

patient

The unique identifier of the ABCD dataset

...

Cortical surface areas of various ROIs (mm^2, I think)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of cortical thickness data from the NIMH Data archive. The original file used was mrisdp10201.txt The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_cort_t.

Usage

abcd_cort_t

Format

abcd_cort_t

A data frame with 188 rows and 152 columns:

patient

The unique identifier of the ABCD dataset

...

Cortical thicknesses of various ROIs (mm^3, I think)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of depression data from the NIMH Data archive. The original file used was pdem02.txt. The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_cbcl_depress.

Usage

abcd_depress

Format

abcd_depress

A data frame with 275 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

cbcl_depress_r

Ordinal value of impairment on CBCL anxiety, either 0 (no impairment), 1 (borderline clinical), or 2 (clinically impaired)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Like abcd_income, but with no NAs in patient column

Usage

abcd_h_income

Format

abcd_income

A data frame with 300 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

household_income

Household income in 3 category levels (low = 1, medium = 2, high = 3)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of income data from the NIMH Data archive. The original file used was pdem02.txt The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_income.

Usage

abcd_income

Format

abcd_income

A data frame with 300 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

household_income

Household income in 3 category levels (low = 1, medium = 2, high = 3)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of pubertal status data from the NIMH Data archive. The original files used were abcd_ssphp01.txt and abcd_ssphy01.txt. The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_pubertal_status.

Usage

abcd_pubertal

Format

abcd_pubertal

A data frame with 275 rows and 2 columns:

patient

The unique identifier of the ABCD dataset

pubertal_status

Average reported pubertal status between child and parent (1-5 categorical scale)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

A randomly shuffled and anonymized copy of subcortical volume data from the NIMH Data archive. The original file used was smrip10201.txt The file was pre-processed by the abcdutils package (https://github.com/BRANCHlab/abcdutils) function get_subc_v.

Usage

abcd_subc_v

Format

abcd_subc_v

A data frame with 174 rows and 31 columns:

patient

The unique identifier of the ABCD dataset

...

Subcortical volumes of various ROIs (mm^3, I think)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Add rows to a settings_df

Usage

add_settings_df_rows(
  sdf,
  n_solutions = 0,
  min_removed_inputs = 0,
  max_removed_inputs = sum(startsWith(colnames(sdf), "inc_")) - 1,
  dropout_dist = "exponential",
  min_alpha = NULL,
  max_alpha = NULL,
  min_k = NULL,
  max_k = NULL,
  min_t = NULL,
  max_t = NULL,
  alpha_values = NULL,
  k_values = NULL,
  t_values = NULL,
  possible_snf_schemes = c(1, 2, 3),
  clustering_algorithms = NULL,
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  dfl = NULL,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10,
  allow_duplicates = FALSE
)

Arguments

Value

A settings data frame

Description

Defunct function to create an ARI heatmap. Please use meta_cluster_heatmap() instead.

Usage

adjusted_rand_index_heatmap(
  aris,
  order = NULL,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  log_graph = FALSE,
  scale_diag = "none",
  min_colour = "#282828",
  max_colour = "firebrick2",
  col = circlize::colorRamp2(c(min(aris), max(aris)), c(min_colour, max_colour)),
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the pairwise adjusted Rand indices (similarities) between the cluster solutions of the provided solutions data frame.

Description

Mock age data

Usage

age_df

Format

age_df

A data frame with 200 rows and 2 columns:

patient_id

Random three-digit number uniquely identifying the patient

age

Mock age feature

Source

This data came from the SNFtool package, with slight modifications.

Description

This alluvial plot shows how observations in a similarity matrix could have been clustered over a set of clustering functions.

Usage

alluvial_cluster_plot(
  cluster_sequence,
  similarity_matrix,
  dl = NULL,
  data = NULL,
  key_outcome,
  key_label = key_outcome,
  extra_outcomes = NULL,
  title = NULL
)

Arguments

Value

An alluvial plot (class "gg" and "ggplot") showing distribution of a feature across varying number cluster solutions.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 1)

sol_df <- batch_snf(input_dl, sc, return_sim_mats = TRUE)

sim_mats <- sim_mats_list(sol_df)

clust_fn_sequence <- list(spectral_two, spectral_four)

alluvial_cluster_plot(
    cluster_sequence = clust_fn_sequence,
    similarity_matrix = sim_mats[[1]],
    dl = input_dl,
    key_outcome = "gender", # the name of the feature of interest
    key_label = "Gender", # how the feature of interest should be displayed
    extra_outcomes = "diagnosis", # more features to plot but not colour by
    title = "Gender Across Cluster Counts"
)

Description

Like the mock data frame "abcd_colour", but with "unique_id" as the "uid".

Usage

anxiety

Format

anxiety

A data frame with 275 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

cbcl_anxiety_r

Ordinal value of impairment on CBCL anxiety, either 0 (no impairment), 1 (borderline clinical), or 2 (clinically impaired)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Arrange rows in an object

Usage

arrange(.data, ...)

Arguments

Value

Object with arrangeed columns.

Description

This function coerces non-ari_matrix class objects into ari_matrix class objects.

Usage

as_ari_matrix(x)

Arguments

Value

An ari_matrix class object.

Description

This function coerces non-data_list class objects into data_list class objects.

Usage

as_data_list(x)

Arguments

Value

A data_list class object.

Description

This function coerces non-settings_df class objects into settings_df class objects.

Usage

as_settings_df(x)

Arguments

Value

A settings_df class object.

Description

This function converts non-sim_mats_list class objects into sim_mats_list class objects.

Usage

as_sim_mats_list(x)

Arguments

Value

A sim_mats_list class object.

Description

This function coerces non-snf_config class objects into snf_config class objects.

Usage

as_snf_config(x)

Arguments

Value

A snf_config class object.

Description

This function converts non-weights_matrix objects into weights_matrix class objects.

Usage

as_weights_matrix(x)

Arguments

Value

A weights_matrix class object.

Description

Horizontally joins data frames within a data list into a single data frame, using the uid attribute as the joining key.

Usage

## S3 method for class 'data_list'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

dl_df A data.frame class object with all the features and observations of dl.

Description

Coerce a ext_solutions_df class object into a data.frame class object

Usage

## S3 method for class 'ext_solutions_df'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  keep_attributes = FALSE,
  ...
)

Arguments

Value

A data.frame class object with all the columns of x and its contained solutions data frame.

Description

Coerce a solutions_df class object into a data.frame class object

Usage

## S3 method for class 'solutions_df'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  keep_attributes = FALSE,
  ...
)

Arguments

Value

A data.frame class object with all the columns of x and its contained solutions data frame.

Description

Coerce a clust_fns_list class object into a list class object

Usage

## S3 method for class 'clust_fns_list'
as.list(x, ...)

Arguments

Value

A list class object with all the functions of x.

Description

Coerce a data_list class object into a list class object

Usage

## S3 method for class 'data_list'
as.list(x, ...)

Arguments

Value

A list class object with all the objects of x.

Description

Coerce a dist_fns_list class object into a list class object

Usage

## S3 method for class 'dist_fns_list'
as.list(x, ...)

Arguments

Value

A list class object with all the functions of x.

Description

Coerce a weights_matrix class object into a matrix class object

Usage

## S3 method for class 'weights_matrix'
as.matrix(x, ...)

Arguments

Value

A matrix and array class object.

Description

Collapse a data frame and/or a data list into a single data frame

Usage

assemble_data(data, dl)

Arguments

Value

A class "data.frame" object containing all the features of the provided data frame and/or data list.

Description

Heatmap of pairwise associations between features

Usage

assoc_pval_heatmap(
  correlation_matrix,
  scale_diag = "max",
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_row_names = TRUE,
  show_column_names = TRUE,
  show_heatmap_legend = FALSE,
  confounders = NULL,
  out_of_models = NULL,
  annotation_colours = NULL,
  labels_colour = NULL,
  split_by_domain = FALSE,
  dl = NULL,
  significance_stars = TRUE,
  slice_font_size = 8,
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the pairwise associations between features from the provided correlation_matrix.

Examples

#data_list <- data_list(
#    list(income, "household_income", "demographics", "ordinal"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    list(fav_colour, "favourite_colour", "demographics", "categorical"),
#    list(anxiety, "anxiety", "behaviour", "ordinal"),
#    list(depress, "depressed", "behaviour", "ordinal"),
#    uid = "unique_id"
#)
#
#assoc_pval_matrix <- calc_assoc_pval_matrix(data_list)
#ap_heatmap <- assoc_pval_heatmap(assoc_pval_matrix)

Description

Given a single row of a solutions data frame and data provided through a data list, this function will return a series of bar and/or jitter plots based on feature types.

Usage

auto_plot(
  sol_df_row = NULL,
  dl = NULL,
  cluster_df = NULL,
  return_plots = TRUE,
  save = NULL,
  jitter_width = 6,
  jitter_height = 6,
  bar_width = 6,
  bar_height = 6,
  verbose = FALSE
)

Arguments

Value

By default, returns a list of plots (class "gg", "ggplot") with one plot for every feature in the provided data list and/or target list. If return_plots is FALSE, will instead return a single "data.frame" object containing every provided feature for every observation in long format.

Description

Bar plot separating a feature by cluster

Usage

bar_plot(df, feature)

Arguments

Value

A bar plot (class "gg", "ggplot") showing the distribution of a feature across clusters.

Description

This is the core function of the metasnf package. Using the information stored in a settings_df (see ?settings_df) and a data list (see ?data_list), run repeated complete SNF pipelines to generate a broad space of post-SNF cluster solutions.

Usage

batch_snf(dl, sc, processes = 1, return_sim_mats = FALSE, sim_mats_dir = NULL)

Arguments

Value

By default, returns a solutions data frame (class "data.frame"), a a data frame containing one row for every row of the provided settings matrix, all the original columns of that settings data frame, and new columns containing the assigned cluster of each observation from the cluster solution derived by that row's settings. If return_sim_mats is TRUE, the function will instead return a list containing the solutions data frame as well as a list of the final similarity matrices (class "matrix") generated by SNF for each row of the settings data frame. If suppress_clustering is TRUE, the solutions data frame will not be returned in the output.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 3)

# A solutions data frame without similarity matrices:
sol_df <- batch_snf(input_dl, sc)

# A solutions data frame with similarity matrices:
# sol_df <- batch_snf(input_dl, sc, return_sim_mats = TRUE)
# sim_mats_list(sol_df)

Description

Run SNF clustering pipeline on a list of subsampled data lists.

Usage

batch_snf_subsamples(
  dl_subsamples,
  sc,
  processes = 1,
  return_sim_mats = FALSE,
  sim_mats_dir = NULL,
  verbose = TRUE
)

Arguments

Value

By default, returns a one-element list: cluster_solutions, which is itself a list of cluster solution data frames corresponding to each of the provided data list subsamples. Setting the parameters return_sim_mats and return_solutions to TRUE will turn the result of the function to a three-element list containing the corresponding solutions data frames and final fused similarity matrices of those cluster solutions, should you require these objects for your own stability calculations.

Examples

# my_dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(my_dl, n_solutions = 5, max_k = 40)
# 
# my_dl_subsamples <- subsample_dl(
#     my_dl,
#     n_subsamples = 20,
#     subsample_fraction = 0.85
# )
# 
# batch_subsample_results <- batch_snf_subsamples(
#     my_dl_subsamples,
#     sc,
#     verbose = TRUE
# )

Description

This function constructs an ari_matrix class object from a solutions_df class object. The ARI matrix stores pairwise adjusted Rand indices for all cluster solutions as well as a numeric order for the solutions data frame based on the hierarchical clustering of the ARI matrix.

Usage

calc_aris(
  sol_df,
  processes = 1,
  verbose = FALSE,
  dist_method = "euclidean",
  hclust_method = "complete"
)

Arguments

Value

om_aris ARIs between clustering solutions of an solutions data frame

Examples

dl <- data_list(
    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    uid = "unique_id"
)

sc <- snf_config(dl, n_solutions = 3)
sol_df <- batch_snf(dl, sc)
calc_aris(sol_df)

Description

Calculate p-values for all pairwise associations of features in a data list

Usage

calc_assoc_pval_matrix(dl, verbose = FALSE, cat_test = "chi_squared")

Arguments

Value

A "matrix" class object containing pairwise association p-values between the features in the provided data list.

Examples

data_list <- data_list(
    list(income, "household_income", "demographics", "ordinal"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

assoc_pval_matrix <- calc_assoc_pval_matrix(data_list)

Description

Normalized mutual information scores can be used to indirectly measure how important a feature may have been in producing a cluster solution. This function will calculate the normalized mutual information between cluster solutions in a solutions data frame as well as cluster solutions created by including only a single feature from a provided data list, but otherwise using all the same hyperparameters as specified in the original SNF config. Note that NMIs can be calculated between two cluster solutions regardless of what features were actually used to create those cluster solutions. For example, a feature that was not involved in producing a particular cluster solution may still have a high NMI with that cluster solution (typically because it was highly correlated with a different feature that was used).

Usage

calc_nmis(
  dl,
  sol_df,
  transpose = TRUE,
  ignore_inclusions = TRUE,
  verbose = FALSE
)

Arguments

Value

A "data.frame" class object containing one row for every feature in the provided data list and one column for every solution in the provided solutions data frame. Populated values show the calculated NMI score for each feature-solution combination.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 2)

sol_df <- batch_snf(input_dl, sc)

calc_nmis(input_dl, sol_df)

Description

Calculate coclustering data.

Usage

calculate_coclustering(subsample_solutions, sol_df, verbose = FALSE)

Arguments

Value

A list containing the following components:

• cocluster_dfs: A list of data frames, one per cluster solution, that shows the number of times that every pair of observations in the original cluster solution occurred in the same subsample, the number of times that every pair clustered together in a subsample, and the corresponding fraction of times that every pair clustered together in a subsample.

• cocluster_ss_mats: The number of times every pair of observations occurred in the same subsample, formatted as a pairwise matrix.

• cocluster_sc_mats: The number of times every pair of observations occurred in the same cluster, formatted as a pairwise matrix.

• cocluster_cf_mats: The fraction of times every pair of observations occurred in the same cluster, formatted as a pairwise matrix.

• cocluster_summary: Specifically among pairs of observations that clustered together in the original full cluster solution, what fraction of those pairs remained clustered together throughout the subsample solutions. This information is formatted as a data frame with one row per cluster solution.

Examples

# my_dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(my_dl, n_solutions = 5, max_k = 40)
# 
# sol_df <- batch_snf(my_dl, sc)
# 
# my_dl_subsamples <- subsample_dl(
#     my_dl,
#     n_subsamples = 20,
#     subsample_fraction = 0.85
# )
# 
# batch_subsample_results <- batch_snf_subsamples(
#     my_dl_subsamples,
#     sc,
#     verbose = TRUE
# )
# 
# coclustering_results <- calculate_coclustering(
#     batch_subsample_results,
#     sol_df,
#     verbose = TRUE
# )

Description

This is the same data as diagnosis_df, with renamed features and columns.

Usage

cancer_diagnosis_df

Format

cancer_diagnosis_df

A data frame with 200 rows and 2 columns:

patient_id

Random three-digit number uniquely identifying the patient

diagnosis

Mock cancer diagnosis feature (1, 2, or 3)

Source

This data came from the SNFtool package, with slight modifications.

Description

This is an internal function meant to be used to by the assoc_pval_heatmap function.

Usage

cell_significance_fn(data)

Arguments

Value

cell_fn Another function that is well-formatted for usage as the cell_fun argument in ComplexHeatmap::Heatmap.

Description

Helper function to stop annotation building when no data was provided

Usage

check_dataless_annotations(annotation_requests, data)

Arguments

Value

Does not return any value. This function just raises an error when annotations are requested without any provided data for a heatmap.

Description

Check for ComplexHeatmap and circlize dependencies

Usage

check_hm_dependencies()

Value

Does not return any value. This function just checks that the ComplexHeatmap and circlize packages are installed.

Description

Check to see if similarity matrices in a list have the following properties:

1. The maximum value in the entire matrix is 0.5

2. Every value in the diagonal is 0.5

Usage

check_similarity_matrices(similarity_matrices)

Arguments

Value

valid_matrices Boolean indicating if properties are met by all similarity matrices

Description

These functions can be used when building a metasnf clustering functions list. Each function converts a similarity matrix (matrix class object) to a cluster solution (numeric vector). Note that these functions (or custom clustering functions) cannot accept number of clusters as a parameter; this value must be built into the function itself if necessary.

Usage

spectral_eigen(similarity_matrix)

spectral_rot(similarity_matrix)

spectral_eigen_classic(similarity_matrix)

spectral_rot_classic(similarity_matrix)

spectral_two(similarity_matrix)

spectral_three(similarity_matrix)

spectral_four(similarity_matrix)

spectral_five(similarity_matrix)

spectral_six(similarity_matrix)

spectral_seven(similarity_matrix)

spectral_eight(similarity_matrix)

spectral_nine(similarity_matrix)

spectral_ten(similarity_matrix)

Arguments

Details

• spectral_eigen: Spectral clustering where the number of clusters is based on the eigen-gap heuristic

• spectral_rot: Spectral clustering where the number of clusters is based on the rotation-cost heuristic

• spectral_(C): Spectral clustering for a C-cluster solution.

Value

solution_data A vector of cluster assignments

Description

This function can be used to specify custom clustering algorithms to apply to the final similarity matrices produced by each run of the batch_snf function.

Usage

clust_fns_list(clust_fns = NULL, use_default_clust_fns = FALSE)

Arguments

Value

A list of clustering algorithm functions that can be passed into the batch_snf and generate_settings_list functions.

Examples

# Using just the base clustering algorithms --------------------------------
# This will just contain spectral_eigen and spectral_rot
cfl <- clust_fns_list(use_default_clust_fns = TRUE)

# Adding algorithms provided by the package --------------------------------
# This will contain the base clustering algorithms (spectral_eigen,
#  spectral_rot) as well as two pre-defined spectral clustering functions
#  that force the number of clusters to be two or five
cfl <- clust_fns_list(
     clust_fns = list(
        "two_cluster_spectral" = spectral_two,
        "five_cluster_spectral" = spectral_five
    )
)

# Adding your own algorithms -----------------------------------------------
# This will contain the base and user-provided clustering algorithms
my_clustering_algorithm <- function(similarity_matrix) {
    # your code that converts similarity matrix to clusters here...
}

# Suppress the base algorithms----------------------------------------------
# This will contain only user-provided clustering algorithms
cfl <- clust_fns_list(
    clust_fns = list(
        "two_cluster_spectral" = spectral_two,
        "five_cluster_spectral" = spectral_five
    )
)

Description

This function creates a density plot that shows, for all pairs of observations that originally clustered together, the distribution of the the fractions that those pairs clustered together across subsampled data.

Usage

cocluster_density(cocluster_df)

Arguments

Value

Density plot (class "gg", "ggplot") of the distribution of coclustering across pairs and subsamples of the data.

Examples

# my_dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(my_dl, n_solutions = 5, max_k = 40)
# 
# sol_df <- batch_snf(my_dl, sc)
# 
# my_dl_subsamples <- subsample_dl(
#     my_dl,
#     n_subsamples = 20,
#     subsample_fraction = 0.85
# )
# 
# batch_subsample_results <- batch_snf_subsamples(
#     my_dl_subsamples,
#     sc,
#     verbose = TRUE
# )
# 
# coclustering_results <- calculate_coclustering(
#     batch_subsample_results,
#     sol_df,
#     verbose = TRUE
# )
# 
# cocluster_density(cocluster_dfs[[1]])

Description

Create a heatmap that shows the distribution of observation co-clustering across resampled data.

Usage

cocluster_heatmap(
  cocluster_df,
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_row_names = FALSE,
  show_column_names = FALSE,
  dl = NULL,
  data = NULL,
  left_bar = NULL,
  right_bar = NULL,
  top_bar = NULL,
  bottom_bar = NULL,
  left_hm = NULL,
  right_hm = NULL,
  top_hm = NULL,
  bottom_hm = NULL,
  annotation_colours = NULL,
  min_colour = NULL,
  max_colour = NULL,
  ...
)

Arguments

Value

Heatmap (class "Heatmap" from ComplexHeatmap) object showing the distribution of observation co-clustering across resampled data.

Examples

# my_dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(my_dl, n_solutions = 5, max_k = 40)
# 
# sol_df <- batch_snf(my_dl, sc)
# 
# my_dl_subsamples <- subsample_dl(
#     my_dl,
#     n_subsamples = 20,
#     subsample_fraction = 0.85
# )
# 
# batch_subsample_results <- batch_snf_subsamples(
#     my_dl_subsamples,
#     sc,
#     verbose = TRUE
# )
# 
# coclustering_results <- calculate_coclustering(
#     batch_subsample_results, 
#     sol_df,
#     verbose = TRUE
# )
# 
# cocluster_dfs <- coclustering_results$"cocluster_dfs"
# 
# cocluster_heatmap(
#     cocluster_dfs[[1]],
#     dl = my_dl,
#     top_hm = list(
#         "Income" = "household_income",
#         "Pubertal Status" = "pubertal_status"
#     ),
#     annotation_colours = list(
#         "Pubertal Status" = colour_scale(
#             c(1, 4),
#             min_colour = "black",
#             max_colour = "purple"
#         ),
#         "Income" = colour_scale(
#             c(0, 4),
#             min_colour = "black",
#             max_colour = "red"
#         )
#     )
# )

Description

Defunct function for converting a data list into a data frame. Please use as.data.frame() instead.

Usage

collapse_dl(data_list)

Arguments

Value

A "data.frame"-formatted version of the provided data list.

Description

Given a numeric vector and min and max colour values, return a colour ramp that assigns a colour to each element in the vector. This function is a wrapper for circlize::colorRamp2.'

Usage

colour_scale(data, min_colour, max_colour)

Arguments

Value

A "function" class object that can build a circlize-style colour ramp.

Description

Create a heatmap where each row corresponds to a different set of hyperparameters in an SNF config object. Numeric parameters are scaled normalized and non-numeric parameters are added as heatmap annotations. Rows can be reordered to match prior meta clustering results.

Usage

config_heatmap(
  sc,
  order = NULL,
  hide_fixed = FALSE,
  show_column_names = TRUE,
  show_row_names = TRUE,
  rect_gp = grid::gpar(col = "black"),
  colour_breaks = c(0, 1),
  colours = c("black", "darkseagreen"),
  column_split_vector = NULL,
  row_split_vector = NULL,
  column_split = NULL,
  row_split = NULL,
  column_title = NULL,
  include_weights = TRUE,
  include_settings = TRUE,
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the scaled values of the provided SNF config.

Examples

dl <- data_list(
    list(income, "household_income", "demographics", "ordinal"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    list(fav_colour, "favourite_colour", "demographics", "categorical"),
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

sc <- snf_config(
    dl,
    n_solutions = 10,
    dropout_dist = "uniform"
)

config_heatmap(sc)

Description

Like the mock data frame "abcd_cort_sa", but with "unique_id" as the "uid".

Usage

cort_sa

Format

cort_sa

A data frame with 188 rows and 152 columns:

unique_id

The unique identifier of the ABCD dataset

...

Cortical surface areas of various ROIs (mm^2, I think)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Like the mock data frame "abcd_cort_t", but with "unique_id" as the "uid".

Usage

cort_t

Format

cort_t

A data frame with 188 rows and 152 columns:

unique_id

The unique identifier of the ABCD dataset

...

Cortical thicknesses of various ROIs (mm^3, I think)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

data_list() constructs a data list object which inherits from classes data_list and list. This object is the primary way in which features to be used along the metasnf clustering pipeline are stored. The data list is fundamentally a 2-level nested list object where each inner list contains a data frame and associated metadata for that data frame. The metadata includes the name of the data frame, the 'domain' of that data frame (the broader source of information that the input data frame is capturing, determined by user's domain knowledge), and the type of feature stored in the data frame (continuous, discrete, ordinal, categorical, or mixed).

Usage

data_list(..., uid)

Arguments

Examples

heart_rate_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var1 = c(0.04, 0.1, 0.3),
    var2 = c(30, 2, 0.3)
)

personality_test_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var3 = c(900, 1990, 373),
    var4 = c(509, 2209, 83)
)

survey_response_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var5 = c(1, 3, 3),
    var6 = c(2, 3, 3)
)

city_df <- data.frame(
    patient_id = c("1", "2", "3"),
    var7 = c("toronto", "montreal", "vancouver")
)

# Explicitly (Name each nested list element):
dl <- data_list(
    list(
        data = heart_rate_df,
        name = "heart_rate",
        domain = "clinical",
        type = "continuous"
    ),
    list(
        data = personality_test_df,
        name = "personality_test",
        domain = "surveys",
        type = "continuous"
    ),
    list(
        data = survey_response_df,
        name = "survey_response",
        domain = "surveys",
        type = "ordinal"
    ),
    list(
        data = city_df,
        name = "city",
        domain = "location",
        type = "categorical"
    ),
    uid = "patient_id"
)

# Compact loading
dl <- data_list(
    list(heart_rate_df, "heart_rate", "clinical", "continuous"),
    list(personality_test_df, "personality_test", "surveys", "continuous"),
    list(survey_response_df, "survey_response", "surveys", "ordinal"),
    list(city_df, "city", "location", "categorical"),
    uid = "patient_id"
)

# Printing data list summaries
summary(dl)

# Alternative loading: providing a single list of lists
list_of_lists <- list(
    list(heart_rate_df, "data1", "domain1", "continuous"),
    list(personality_test_df, "data2", "domain2", "continuous")
)

dl <- data_list(
    list_of_lists,
    uid = "patient_id"
)

Description

Like the mock data frame "abcd_depress", but with "unique_id" as the "uid".

Usage

depress

Format

depress

A data frame with 275 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

cbcl_depress_r

Ordinal value of impairment on CBCL anxiety, either 0 (no impairment), 1 (borderline clinical), or 2 (clinically impaired)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

This is the same data as cancer_diagnosis_df, with renamed features and columns.

Usage

diagnosis_df

Format

diagnosis_df

A data frame with 200 rows and 2 columns:

patient_id

Random three-digit number uniquely identifying the patient

diagnosis

Mock diagnosis feature

Source

This data came from the SNFtool package, with slight modifications.

Description

These functions can be used when building a metasnf distance functions list. Each function converts a data frame into to a distance matrix.

Usage

euclidean_distance(df, weights_row)

gower_distance(df, weights_row)

sn_euclidean_distance(df, weights_row)

sew_euclidean_distance(df, weights_row)

hamming_distance(df, weights_row)

Arguments

Details

Functions that work for numeric data:

• euclidean_distance: typical Euclidean distance

• sn_euclidean_distance: Data frame is first standardized and normalized before typical Euclidean distance is applied

• siw_euclidean_distance: Squared (including weights) Euclidean distance, where the weights are also squared

• sew_euclidean_distance: Squared (excluding weights) Euclidean distance, where the weights are not also squared

Functions that work for binary data:

• hamming_distance: typical Hamming distance

Functions that work for any type of data:

• gower_distance: Gower distance (cluster::daisy)

Value

A matrix class object containing pairwise distances.

Description

The distance metrics list object (inherits classes dist_fns_list and list) is a list that stores R functions which can convert a data frame of features into a matrix of pairwise distances. The list is a nested one, where the first layer of the list can hold up to 5 items (one for each of the metasnf recognized feature types, continuous, discrete, ordinal, categorical, and mixed), and the second layer can hold an arbitrary number of distance functions for each of those types.

Usage

dist_fns_list(
  cnt_dist_fns = NULL,
  dsc_dist_fns = NULL,
  ord_dist_fns = NULL,
  cat_dist_fns = NULL,
  mix_dist_fns = NULL,
  automatic_standard_normalize = FALSE,
  use_default_dist_fns = FALSE
)

Arguments

Details

Call ?distance_metrics to see all distance metric functions provided in metasnf.

Value

A distance metrics list object.

Examples

# Using just the base distance metrics  ------------------------------------
dist_fns_list <- dist_fns_list()

# Adding your own metrics --------------------------------------------------
# This will contain only the and user-provided distance function:
cubed_euclidean <- function(df, weights_row) {
    # (your code that converts a data frame to a distance metric here...)
    weights <- diag(weights_row, nrow = length(weights_row))
    weighted_df <- as.matrix(df) %*% weights
    distance_matrix <- weighted_df |>
        stats::dist(method = "euclidean") |>
        as.matrix()
    distance_matrix <- distance_matrix^3
    return(distance_matrix)
}

dist_fns_list <- dist_fns_list(
    cnt_dist_fns = list(
         "my_cubed_euclidean" = cubed_euclidean
    )
)

# Using default base metrics------------------------------------------------
# Call ?distance_metrics to see all distance metric functions provided in
# metasnf. The code below will contain a mix of user-provided and built-in
# distance metric functions.
dist_fns_list <- dist_fns_list(
    cnt_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    dsc_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    ord_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    cat_dist_fns = list(
         "my_distance_metric" = gower_distance
    ),
    mix_dist_fns = list(
         "my_distance_metric" = gower_distance
    ),
    use_default_dist_fns = TRUE
)

Description

Defunct function to summarize a data list. Please use summary() with argument scope = "feature" instead.

Usage

dl_variable_summary(dl)

Arguments

Value

variable_level_summary A data frame containing the name, type, and domain of every variable in a data list.

Description

This function enables manipulating a data_list class object with lapply syntax without removing that object's data_list class attribute. The function will only preserve this attribute if the result of the apply call has a valid data list structure.

Usage

dlapply(X, FUN, ...)

Arguments

Value

If FUN applied to each component of X yields a valid data list, a data list. Otherwise, a list.

Examples

# Convert all UID values to lowercase
dl <- data_list(
    list(abcd_income, "income", "demographics", "discrete"),
    list(abcd_colour, "colour", "likes", "categorical"),
    uid = "patient"
)

dl_lower <- dlapply(
    dl,
    function(x) {
        x$"data"$"uid" <- tolower(x$"data"$"uid")
        return(x)
    }
)

Description

Function to extend dplyr to extended solutions data frame objects

Usage

dplyr_row_slice.ext_solutions_df(data, i, ...)

Arguments

Value

Row sliced object with appropriately preserved attributes.

Description

Function to extend dplyr to solutions data frame objects

Usage

dplyr_row_slice.solutions_df(data, i, ...)

Arguments

Value

Row sliced object with appropriately preserved attributes.

Description

Manhattan plot of feature-cluster association p-values

Usage

esm_manhattan_plot(
  esm,
  neg_log_pval_thresh = 5,
  threshold = NULL,
  point_size = 5,
  jitter_width = 0.1,
  jitter_height = 0.1,
  text_size = 15,
  plot_title = NULL,
  hide_x_labels = FALSE,
  bonferroni_line = FALSE
)

Arguments

Value

A Manhattan plot (class "gg", "ggplot") showing the association p-values of features against each solution in the provided solutions data frame.

Examples

# full_dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     list(anxiety, "anxiety", "behaviour", "ordinal"),
#     list(depress, "depressed", "behaviour", "ordinal"),
#     uid = "unique_id"
# )
# 
# dl <- full_dl[1:3]
# target_dl <- full_dl[4:5]
# 
# set.seed(42)
# sc <- snf_config(
#     dl = dl,
#     n_solutions = 20,
#     min_k = 20,
#     max_k = 50
# )
# 
# sol_df <- batch_snf(dl, sc)
# 
# ext_sol_df <- extend_solutions(
#     sol_df,
#     dl = dl,
#     target = target_dl,
#     min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )
# 
# esm_manhattan <- esm_manhattan_plot(
#     ext_sol_df[1:5, ],
#     neg_log_pval_thresh = 5,
#     threshold = 0.05,
#     point_size = 3,
#     jitter_width = 0.1,
#     jitter_height = 0.1,
#     plot_title = "Feature-Solution Associations",
#     text_size = 14,
#     bonferroni_line = TRUE
# )

Description

Calculate eigengap and rotation-cost estimates of the number of clusters to use when clustering a similarity matrix. This function was adapted from SNFtool::estimateClustersGivenGraph, but scales up the Laplacian operator prior to eigenvalue calculations to minimize the risk of floating point-related errors.

Usage

estimate_nclust_given_graph(W, NUMC = 2:10)

Arguments

Value

A list containing the top two eigengap and rotation-cost estimates for the number of clusters in a given similarity matrix.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 1)
sol_df <- batch_snf(input_dl, sc, return_sim_mats = TRUE)
sim_mat <- sim_mats_list(sol_df)[[1]]
estimate_nclust_given_graph(sim_mat)

Description

Modification of SNFtool mock data frame "Data1"

Usage

expression_df

Format

expression_df

A data frame with 200 rows and 3 columns:

gene_1_expression

Mock gene expression feature

gene_2_expression

Mock gene expression feature

patient_id

Random three-digit number uniquely identifying the patient

Source

This data came from the SNFtool package, with slight modifications.

Description

Extend a solutions data frame to include outcome evaluations

Usage

extend_solutions(
  sol_df,
  target_dl = NULL,
  dl = NULL,
  cat_test = "chi_squared",
  min_pval = 1e-10,
  processes = 1,
  verbose = FALSE
)

Arguments

Value

An extended solutions data frame (ext_sol_df class object) that contains p-value columns for each outcome in the provided data lists

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 2)

sol_df <- batch_snf(input_dl, sc)

ext_sol_df <- extend_solutions(sol_df, input_dl)

Description

Like the mock data frame "abcd_colour", but with "unique_id" as the "uid".

Usage

fav_colour

Format

fav_colour

A data frame with 275 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

colour

Categorical transformation of cbcl_depress.

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Return character vector of features stored in an object

Usage

features(x)

Arguments

Value

A character vector of features in x.

Description

Mock gender data

Usage

gender_df

Format

gender_df

A data frame with 200 rows and 2 columns:

patient_id

Random three-digit number uniquely identifying the patient

gender_df

Mock gene methylation feature

Source

This data came from the SNFtool package, with slight modifications.

Description

Deprecated function for building a clustering algorithms list. Please use clust_fns_list() (or better yet, snf_config()) instead.

Usage

generate_clust_algs_list(..., disable_base = FALSE)

Arguments

Value

A list of clustering algorithm functions that can be passed into the batch_snf and generate_settings_list functions.

Description

Deprecated function for building a distance metrics list. Please use dist_fns_list() (or better yet, snf_config()) instead.

Usage

generate_distance_metrics_list(
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  keep_defaults = TRUE
)

Arguments

Value

A nested and named list of distance metrics functions.

Description

Deprecated function for building a settings matrix. Please use settings_df() instead.

Usage

generate_settings_matrix(...)

Arguments

Value

Raises a deprecated error.

Description

Deprecated function for building extracting cluster solutions from a solutions data frame. Please use t() instead.

This function takes in a single row of a solutions data frame and returns a data frame containing the cluster assignments for each uid. It is similar to get_clusters(), which takes one solutions data frame row and returns a vector of cluster assignments' and get_cluster_solutions(), which takes a solutions data frame with any number of rows and returns a data frame indicating the cluster assignments for each of those rows.

Usage

get_cluster_df(sol_df_row)

Arguments

Value

cluster_df data frame of cluster and uid.

Description

Deprecated function for building extracting cluster solutions from a solutions data frame. Please use t() instead.

This function takes in a solutions data frame and returns a data frame containing the cluster assignments for each uid. It is similar to 'get_clusters(), which takes one solutions data frame row and returns a vector of cluster assignments' and get_cluster_df(), which takes a solutions matrix with only one row and returns a data frame with two columns: "cluster" and "uid" (the UID of the observation).

Usage

get_cluster_solutions(sol_df)

Arguments

Value

A "data.frame" object where each row is an observation and each column (apart from the uid column) indicates the cluster that observation was assigned to for the corresponding solutions data frame row.

Description

Deprecated function for building extracting cluster solutions from a solutions data frame. Please use t() instead.

This function takes in a single row of a solutions data frame and returns a vector containing the cluster assignments for each observation. It is similar to get_cluster_df(), which takes a solutions data frame with only one row and returns a data frame with two columns: "cluster" and "uid" '(the UID of the observation) and get_cluster_solutions(), which takes a solutions data frame with any number of rows and returns a data frame indicating the cluster assignments for each of those rows.

Usage

get_clusters(sol_df_row)

Arguments

Value

clusters Vector of assigned clusters.

Description

This function identifies all observations within a list of data frames that have no missing data across all data frames. This function is useful when constructing data lists of distinct feature sets from the same sample of observations. As data_list() strips away observations with any missing data, distinct sets of observations may be generated by building a data list from the same group of observations over different sets of features. Reducing the pool of observations to only those with complete UIDs first will avoid downstream generation of data lists of differing sizes.

Usage

get_complete_uids(list_of_dfs, uid)

Arguments

Value

A character vector of the UIDs of observations that have complete data across the provided list of data frames.

Examples

complete_uids <- get_complete_uids(
    list(income, pubertal, anxiety, depress),
    uid = "unique_id"
)

income <- income[income$"unique_id" %in% complete_uids, ]
pubertal <- pubertal[pubertal$"unique_id" %in% complete_uids, ]
anxiety <- anxiety[anxiety$"unique_id" %in% complete_uids, ]
depress <- depress[depress$"unique_id" %in% complete_uids, ]

input_dl <- data_list(
    list(income, "income", "demographics", "ordinal"),
    list(pubertal, "pubertal", "demographics", "continuous"),
    uid = "unique_id"
)

target_dl <- data_list(
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

Description

Deprecated function for extracting UIDs from a data list. Please use uids() instead.

Usage

get_dl_uids(dl, prefix = FALSE)

Arguments

Value

A character vector of the UID labels contained in a data list.

Description

Return the row or column ordering present in a heatmap

Usage

get_heatmap_order(heatmap, type = "rows")

Arguments

Value

A numeric vector of the ordering used within the provided ComplexHeatmap "Heatmap" object.

Description

Return the hierarchical clustering order of a matrix

Usage

get_matrix_order(matrix, dist_method = "euclidean", hclust_method = "complete")

Arguments

Value

A numeric vector of the ordering derivied by the specified hierarchical clustering method applied to the provided matrix.

Examples

# dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     list(anxiety, "anxiety", "behaviour", "ordinal"),
#     list(depress, "depressed", "behaviour", "ordinal"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(
#     dl = dl,
#     n_solutions = 20,
#     min_k = 20,
#     max_k = 50
# )
# 
# sol_df <- batch_snf(dl, sc)
# 
# ext_sol_df <- extend_solutions(
#     sol_df,
#     dl = dl,
#     min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )
# 
# # Calculate pairwise similarities between cluster solutions
# sol_aris <- calc_aris(sol_df)
# 
# # Extract hierarchical clustering order of the cluster solutions
# meta_cluster_order <- get_matrix_order(sol_aris)

Description

This function can be used to neatly format the p-values associated with an extended solutions data frame. It can also calculate the negative logs of those p-values to make it easier to interpret large-scale differences.

Usage

get_pvals(ext_sol_df, negative_log = FALSE, keep_summaries = TRUE)

Arguments

Value

A "data.frame" class object Of only the p-value related columns of the provided ext_sol_df.

Description

Following clustering with batch_snf, a matrix of pairwise ARIs that show how related each cluster solution is to each other can be generated by the calc_aris function. Partitioning of the ARI matrix can be done by visual inspection of meta_cluster_heatmap() results or by shiny_annotator. Given the indices of meta cluster boundaries, this function will return a single representative solution from each meta cluster based on maximum average ARI to all other solutions within that meta cluster.

Usage

get_representative_solutions(aris, sol_df, filter_fn = NULL)

Arguments

Value

The provided solutions data frame reduced to just one row per meta cluster defined by the split vector.

Examples

# dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     list(anxiety, "anxiety", "behaviour", "ordinal"),
#     list(depress, "depressed", "behaviour", "ordinal"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(
#     dl = dl,
#     n_solutions = 20,
#     min_k = 20,
#     max_k = 50
# )
# 
# sol_df <- batch_snf(dl, sc)
# 
# ext_sol_df <- extend_solutions(
#     sol_df,
#     dl = dl,
#     min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )
# 
# # Calculate pairwise similarities between cluster solutions
# sol_aris <- calc_aris(sol_df)
# 
# # Extract hierarchical clustering order of the cluster solutions
# meta_cluster_order <- get_matrix_order(sol_aris)
# 
# # Identify meta cluster boundaries with shiny app or trial and error
# # ari_hm <- meta_cluster_heatmap(sol_aris, order = meta_cluster_order)
# # shiny_annotator(ari_hm)
# 
# # Result of meta cluster examination
# split_vec <- c(2, 5, 12, 17)
# 
# ext_sol_df <- label_meta_clusters(ext_sol_df, split_vec, meta_cluster_order)
# 
# # Extracting representative solutions from each defined meta cluster
# rep_solutions <- get_representative_solutions(sol_aris, ext_sol_df)

Description

Like the mock data frame "abcd_h_income", but with "unique_id" as the "uid".

Like the mock data frame "abcd_cort_sa", but with "unique_id" as the "uid".

Usage

income

income

Format

income

A data frame with 300 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

household_income

Household income in 3 category levels (low = 1, medium = 2, high = 3)

income

A data frame with 300 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

household_income

Household income in 3 category levels (low = 1, medium = 2, high = 3)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Given an object, returns TRUE if that object inherits from the data_list class.

Usage

is_data_list(x)

Arguments

Value

TRUE if the object inherits from the data_list class.

Description

Jitter plot separating a feature by cluster

Usage

jitter_plot(df, feature)

Arguments

Value

A jitter+violin plot (class "gg", "ggplot") showing the distribution of a feature across clusters.

Description

Given a solutions data frame or extended solutions data frame class object and a numeric vector indicating which rows correspond to which meta clusters, assigns meta clustering information to the "meta_clusters" attribute of the data frame.

Usage

label_meta_clusters(sol_df, split_vector, order = NULL)

Arguments

Value

A solutions data frame with a populated "meta_clusters" attribute.

Examples

#dl <- data_list(
#    list(cort_sa, "cortical_surface_area", "neuroimaging", "continuous"),
#    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#    list(income, "household_income", "demographics", "continuous"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
#set.seed(42)
#my_sc <- snf_config(
#    dl = dl,
#    n_solutions = 20,
#    min_k = 20,
#    max_k = 50
#)
#
#sol_df <- batch_snf(dl, my_sc)
#
#sol_df
#
#sol_aris <- calc_aris(sol_df)
#
#meta_cluster_order <- get_matrix_order(sol_aris)
#
## `split_vec` found by iteratively plotting ari_hm or by ?shiny_annotator()
#split_vec <- c(6, 10, 16)
#ari_hm <- meta_cluster_heatmap(
#    sol_aris,
#    order = meta_cluster_order,
#    split_vector = split_vec
#)
#
#mc_sol_df <- label_meta_clusters(
#    sol_df,
#    order = meta_cluster_order,
#    split_vector = split_vec
#)
#
#mc_sol_df

Description

Given a solutions data frame containing clustered observations and a data list containing those clustered observations as well as additional to-be-clustered observations, this function will re-run SNF to generate a similarity matrix of all observations and use the label propagation algorithm to assigned predicted clusters to the unclustered observations.

Usage

label_propagate(partial_sol_df, full_dl, verbose = FALSE)

Arguments

Value

A data frame with one row per observation containing a column for UIDs, a column for whether the observation was in the train (original) or test (held out) set, and one column per row of the solutions data frame indicating the original and propagated clusters.

Examples

## Function to identify obervations with complete data
#uids_with_complete_obs <- get_complete_uids(
#    list(subc_v, income, pubertal, anxiety, depress),
#    uid = "unique_id"
#)
#
## Dataframe assigning 80% of observations to train and 20% to test
#train_test_split <- train_test_assign(
#    train_frac = 0.8,
#    uids = uids_with_complete_obs
#)
#
## Pulling the training and testing observations specifically
#train_obs <- train_test_split$"train"
#test_obs <- train_test_split$"test"
#
## Partition a training set
#train_subc_v <- subc_v[subc_v$"unique_id" %in% train_obs, ]
#train_income <- income[income$"unique_id" %in% train_obs, ]
#train_pubertal <- pubertal[pubertal$"unique_id" %in% train_obs, ]
#train_anxiety <- anxiety[anxiety$"unique_id" %in% train_obs, ]
#train_depress <- depress[depress$"unique_id" %in% train_obs, ]
#
## Partition a test set
#test_subc_v <- subc_v[subc_v$"unique_id" %in% test_obs, ]
#test_income <- income[income$"unique_id" %in% test_obs, ]
#test_pubertal <- pubertal[pubertal$"unique_id" %in% test_obs, ]
#test_anxiety <- anxiety[anxiety$"unique_id" %in% test_obs, ]
#test_depress <- depress[depress$"unique_id" %in% test_obs, ]
#
## Find cluster solutions in the training set
#train_dl <- data_list(
#    list(train_subc_v, "subc_v", "neuroimaging", "continuous"),
#    list(train_income, "household_income", "demographics", "continuous"),
#    list(train_pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
## We'll pick a solution that has good separation over our target features
#train_target_dl <- data_list(
#    list(train_anxiety, "anxiety", "behaviour", "ordinal"),
#    list(train_depress, "depressed", "behaviour", "ordinal"),
#    uid = "unique_id"
#)
#
#sc <- snf_config(
#    train_dl,
#    n_solutions = 5,
#    min_k = 10,
#    max_k = 30
#)
#
#train_sol_df <- batch_snf(
#    train_dl,
#    sc,
#    return_sim_mats = TRUE
#)
#
#ext_sol_df <- extend_solutions(
#    train_sol_df,
#    train_target_dl
#)
#
## Determining solution with the lowest minimum p-value
#lowest_min_pval <- min(ext_sol_df$"min_pval")
#which(ext_sol_df$"min_pval" == lowest_min_pval)
#top_row <- ext_sol_df[1, ]
#
## Propagate that solution to the observations in the test set
## data list below has both training and testing observations
#full_dl <- data_list(
#    list(subc_v, "subc_v", "neuroimaging", "continuous"),
#    list(income, "household_income", "demographics", "continuous"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
## Use the solutions data frame from the training observations and the data list
## from the training and testing observations to propagate labels to the test observations
#propagated_labels <- label_propagate(top_row, full_dl)
#
#propagated_labels_all <- label_propagate(ext_sol_df, full_dl)
#
#head(propagated_labels_all)
#tail(propagated_labels_all)

Description

Given a data list to correct and another data list of categorical features to linearly adjust for, corrects the first data list based on the residuals of the linear model relating the numeric features in the first data list to the unwanted signal features in the second data list.

Usage

linear_adjust(dl, unwanted_signal_list, sig_digs = NULL)

Arguments

Value

A data list ("list") in which each data component has been converted to contain residuals off of the linear model built against the features in the unwanted_signal_list.

Examples

has_tutor <- sample(c(1, 0), size = 9, replace = TRUE)
math_score <- 70 + 30 * has_tutor + rnorm(9, mean = 0, sd = 5)

math_df <- data.frame(uid = paste0("id_", 1:9), math = math_score)
tutor_df <- data.frame(uid = paste0("id_", 1:9), tutor = has_tutor)

dl <- data_list(
    list(math_df, "math_score", "school", "continuous"),
    uid = "uid"
)

adjustment_dl <- data_list(
    list(tutor_df, "tutoring", "school", "categorical"),
    uid = "uid"
)

adjusted_dl <- linear_adjust(dl, adjustment_dl)

adjusted_dl[[1]]$"data"$"math"

# Equivalent to:
as.numeric(resid(lm(math_score ~ has_tutor)))

Description

Given a data frame of representative meta cluster solutions (see get_representative_solutions(), returns a Manhattan plot for showing feature separation across all features in provided data/target lists.

Usage

mc_manhattan_plot(
  ext_sol_df,
  dl = NULL,
  target_dl = NULL,
  variable_order = NULL,
  neg_log_pval_thresh = 5,
  threshold = NULL,
  point_size = 5,
  text_size = 20,
  plot_title = NULL,
  xints = NULL,
  hide_x_labels = FALSE,
  domain_colours = NULL
)

Arguments

Value

A Manhattan plot (class "gg", "ggplot") showing the association p-values of features against each solution in the provided solutions data frame, stratified by meta cluster label.

Examples

# dl <- data_list(
#     list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#     list(income, "household_income", "demographics", "continuous"),
#     list(pubertal, "pubertal_status", "demographics", "continuous"),
#     list(anxiety, "anxiety", "behaviour", "ordinal"),
#     list(depress, "depressed", "behaviour", "ordinal"),
#     uid = "unique_id"
# )
# 
# sc <- snf_config(
#     dl = dl,
#     n_solutions = 20,
#     min_k = 20,
#     max_k = 50
# )
# 
# sol_df <- batch_snf(dl, sc)
# 
# ext_sol_df <- extend_solutions(
#     sol_df,
#     dl = dl,
#     min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )
# 
# # Calculate pairwise similarities between cluster solutions
# sol_aris <- calc_aris(sol_df)
# 
# # Extract hierarchical clustering order of the cluster solutions
# meta_cluster_order <- get_matrix_order(sol_aris)
# 
# # Identify meta cluster boundaries with shiny app or trial and error
# # ari_hm <- meta_cluster_heatmap(sol_aris, order = meta_cluster_order)
# # shiny_annotator(ari_hm)
# 
# # Result of meta cluster examination
# split_vec <- c(2, 5, 12, 17)
# 
# ext_sol_df <- label_meta_clusters(ext_sol_df, split_vec, meta_cluster_order)
# 
# # Extracting representative solutions from each defined meta cluster
# rep_solutions <- get_representative_solutions(sol_aris, ext_sol_df)
# 
# mc_manhattan <- mc_manhattan_plot(
#     rep_solutions,
#     dl = dl,
#     point_size = 3,
#     text_size = 12,
#     plot_title = "Feature-Meta Cluster Associations",
#     threshold = 0.05,
#     neg_log_pval_thresh = 5
# )
# 
# mc_manhattan

Description

This helper function combines all data frames in a single-level list into a single data frame.

Usage

merge_df_list(df_list, join = "inner", uid = "uid", no_na = FALSE)

Arguments

Value

Inner join of all data frames in list.

Examples

merge_df_list(list(income, pubertal), uid = "unique_id")

Description

Join two data lists with the same components (data frames) but separate observations. To instead merge two data lists that have the same observations but different components, simply use c().

Usage

## S3 method for class 'data_list'
merge(x, y, ...)

Arguments

Value

A data list ("list"-class object) containing the observations of both provided data lists.

Description

Merge method for SNF config objects

Usage

## S3 method for class 'snf_config'
merge(x, y, reset_indices = TRUE, ...)

Arguments

Value

An SNF config combining the rows of both prior configurations.

Description

Heatmap of pairwise adjusted rand indices between solutions

Usage

meta_cluster_heatmap(
  aris,
  order = NULL,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  log_graph = FALSE,
  scale_diag = "none",
  min_colour = "#282828",
  max_colour = "firebrick2",
  col = circlize::colorRamp2(c(min(aris), max(aris)), c(min_colour, max_colour)),
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the pairwise adjusted Rand indices (similarities) between the cluster solutions of the provided solutions data frame.

Examples

#dl <- data_list(
#    list(cort_sa, "cortical_surface_area", "neuroimaging", "continuous"),
#    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#    list(income, "household_income", "demographics", "continuous"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
#set.seed(42)
#my_sc <- snf_config(
#    dl = dl,
#    n_solutions = 20,
#    min_k = 20,
#    max_k = 50
#)
#
#sol_df <- batch_snf(dl, my_sc)
#
#sol_df
#
#sol_aris <- calc_aris(sol_df)
#
#meta_cluster_order <- get_matrix_order(sol_aris)
#
## `split_vec` found by iteratively plotting ari_hm or by ?shiny_annotator()
#split_vec <- c(6, 10, 16)
#ari_hm <- meta_cluster_heatmap(
#    sol_aris,
#    order = meta_cluster_order,
#    split_vector = split_vec
#)

Description

Modification of SNFtool mock data frame "Data2"

Usage

methylation_df

Format

methylation_df

A data frame with 200 rows and 3 columns:

gene_1_expression

Mock gene methylation feature

gene_2_expression

Mock gene methylation feature

patient_id

Random three-digit number uniquely identifying the patient

Source

This data came from the SNFtool package, with slight modifications.

Description

Extract number of features stored in an object

Usage

n_features(x)

Arguments

Value

The number of features in x.

Description

Extract number of observations stored in an object

Usage

n_observations(x)

Arguments

Value

The number of observations in x.

Description

Constructor for solutions_df class object

Usage

new_solutions_df(sol_dfl)

Arguments

Value

A solutions_df class object.

Description

Custom formatted print for weights matrices that outputs information about feature weights functions to the console.

Usage

## S3 method for class 'ari_matrix'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for clustering functions list objects that outputs information about the contained clustering functions to the console.

Usage

## S3 method for class 'clust_fns_list'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for data list objects that outputs information about the contained observations and components to the console.

Usage

## S3 method for class 'data_list'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for distance metrics list objects that outputs information about the contained distance metrics to the console.

Usage

## S3 method for class 'dist_fns_list'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for extended solutions data frame class objects.

Usage

## S3 method for class 'ext_solutions_df'
print(x, n = NULL, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for settings data frame that outputs information about SNF hyperparameters to the console.

Usage

## S3 method for class 'settings_df'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for SNF config

Usage

## S3 method for class 'snf_config'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for weights matrices that outputs information about feature weights functions to the console.

Usage

## S3 method for class 'solutions_df'
print(x, n = NULL, tips = TRUE, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for transposed solutions data frame class objects.

Usage

## S3 method for class 't_ext_solutions_df'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for transposed solutions data frame class objects.

Usage

## S3 method for class 't_solutions_df'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Custom formatted print for weights matrices that outputs information about feature weights functions to the console.

Usage

## S3 method for class 'weights_matrix'
print(x, ...)

Arguments

Value

Function prints to console but does not return any value.

Description

Like the mock data frame "abcd_pubertal", but with "unique_id" as the "uid".

Usage

pubertal

Format

pubertal

A data frame with 275 rows and 2 columns:

unique_id

The unique identifier of the ABCD dataset

pubertal_status

Average reported pubertal status between child and parent (1-5 categorical scale)

Source

Though this data is no longer "real" ABCD data, the reference for using ABCD as a data source is below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

Description

Heatmap of p-values

Usage

pval_heatmap(
  ext_sol_df,
  order = NULL,
  cluster_columns = TRUE,
  cluster_rows = FALSE,
  show_row_names = FALSE,
  show_column_names = TRUE,
  min_colour = "red2",
  max_colour = "white",
  legend_breaks = c(0, 1),
  col = circlize::colorRamp2(legend_breaks, c(min_colour, max_colour)),
  heatmap_legend_param = list(color_bar = "continuous", title = "p-value", at = c(0, 1)),
  rect_gp = grid::gpar(col = "black"),
  column_split_vector = NULL,
  row_split_vector = NULL,
  column_split = NULL,
  row_split = NULL,
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the provided p-values.

Examples

#dl <- data_list(
#    list(income, "household_income", "demographics", "ordinal"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    list(fav_colour, "favourite_colour", "demographics", "categorical"),
#    list(anxiety, "anxiety", "behaviour", "ordinal"),
#    list(depress, "depressed", "behaviour", "ordinal"),
#    uid = "unique_id"
#)
#
#sc <- snf_config(
#    dl,
#    n_solutions = 4,
#    dropout_dist = "uniform",
#    max_k = 50
#)
#
#sol_df <- batch_snf(dl, sc)
#
#ext_sol_df <- extend_solutions(sol_df, dl)
#
#pval_heatmap(ext_sol_df)

Description

These functions calculate conventional metrics of cluster solution quality.

Usage

calculate_silhouettes(sol_df)

calculate_dunn_indices(sol_df)

calculate_db_indices(sol_df)

Arguments

Details

calculate_silhouettes: A wrapper for cluster::silhouette that calculates silhouette scores for all cluster solutions in a provided solutions data frame. Silhouette values range from -1 to +1 and indicate an overall ratio of how close together observations within a cluster are to how far apart observations across clusters are. You can learn more about interpreting the results of this function by calling ?cluster::silhouette.

calculate_dunn_indices: A wrapper for clv::clv.Dunn that calculates Dunn indices for all cluster solutions in a provided solutions data frame. Dunn indices, like silhouette scores, similarly reflect similarity within clusters and separation across clusters. You can learn more about interpreting the results of this function by calling ?clv::clv.Dunn.

calculate_db_indices: A wrapper for clv::clv.Davies.Bouldin that calculates Davies-Bouldin indices for all cluster solutions in a provided solutions data frame. These values can be interpreted similarly as those above. You can learn more about interpreting the results of this function by calling ?clv::clv.Davies.Bouldin.

Value

A list of silhouette class objects, a vector of Dunn indices, or a vector of Davies-Bouldin indices depending on which function was used.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 5)

sol_df <- batch_snf(input_dl, sc, return_sim_mats = TRUE)

# calculate Davies-Bouldin indices
davies_bouldin_indices <- calculate_db_indices(sol_df)

# calculate Dunn indices
dunn_indices <- calculate_dunn_indices(sol_df)

# calculate silhouette scores
silhouette_scores <- calculate_silhouettes(sol_df)

Description

Helper function to contribute to rows within the settings data frame. Number of columns removed follows a uniform or exponential probability distribution.

Usage

random_removal(
  columns,
  min_removed_inputs,
  max_removed_inputs,
  dropout_dist = "exponential"
)

Arguments

Value

inclusions_df data frame that can be rbind'ed to the settings_df

Description

Row-binding of solutions data frame class objects.

Usage

## S3 method for class 'ext_solutions_df'
rbind(..., reset_indices = FALSE)

Arguments

Value

An ext_solutions_df class object.

Description

Row-binding of solutions data frame class objects.

Usage

## S3 method for class 'solutions_df'
rbind(..., reset_indices = FALSE)

Arguments

Value

A solutions_df class object.

Description

Rename features in a data list

Usage

rename_dl(dl, name_mapping)

Arguments

Value

A data list ("list"-class object) with adjusted feature names.

Examples

library(metasnf)

dl <- data_list(
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

summary(dl, "feature")

name_changes <- c(
    "anxiety_score" = "cbcl_anxiety_r",
    "depression_score" = "cbcl_depress_r"
)

dl <- rename_dl(dl, name_changes)

summary(dl, "feature")

Description

Like sample, but when given a single value x, returns back that single value instead of a random value from 1 to x.

Usage

resample(x, ...)

Arguments

Value

Numeric vector result of running base::sample.

Description

Save a heatmap object to a file

Usage

save_heatmap(heatmap, path, width = 480, height = 480, res = 100)

Arguments

Value

Does not return any value. Saves heatmap to file.

Description

The settings_df is a data frame whose rows completely specify the hyperparameters and decisions required to transform individual input data frames (found in a data list, see ?data_list) into a single similarity matrix through SNF. The format of the settings data frame is as follows:

• A column named "solution": This column is used to keep track of the rows and should have integer values only.

• A column named "alpha": This column contains the value of the alpha hyperparameter that will be used on that run of the SNF pipeline.

• A column named "k": Like above, but for the K (nearest neighbours) hyperparameter.

• A column named "t": Like above, but for the t (number of iterations) hyperparameter.

• A column named "snf_scheme": Which of 3 pre-defined schemes will be used to integrate the data frames of the data list into a final fused network. The purpose of varying these schemes is primarily to increase the diversity of the generated cluster solutions.

  • A value of 1 corresponds to the "individual" scheme, in which all data frames are directly merged by SNF into the final fused network. This scheme corresponds to the approach shown in the original SNF paper.

  • A value of 2 corresponds to the "two-step" scheme, in which all data frames witin a domain are first merged into a domain-specific fused network. Next, domain-specific networks are fused once more by SNF into the final fused network. This scheme is useful for fairly re-weighting SNF pipelines with unequal numbers of data frames across domains.

  • A value of 3 corresponds to the "domain" scheme, in which all data frames within a domain are first concatenated into a single domain- specific data frame before being merged by SNF into the final fused network. This approach serves as an alternative way to re-weight SNF pipelines with unequal numbers of data frames across domains. You can learn more about this parameter here: https://branchlab.github.io/metasnf/articles/snf_schemes.html.

• A column named "clust_alg": Specification of which clustering algorithm will be applied to the final similarity matrix. By default, this column can take on the integer values 1 or 2, which correspond to spectral clustering where the number of clusters is determined by the eigen-gap or rotation cost heuristic respectively. You can learn more about this parameter here: https://branchlab.github.io/metasnf/articles/clustering_algorithms.html.

• A column named "cnt_dist": Specification of which distance metric will be used for data frames of purely continuous data. You can learn about this metric and its defaults here: https://branchlab.github.io/metasnf/articles/distance_metrics.html

• A column named "dsc_dist": Like above, but for discrete data frames.

• A column named "ord_dist": Like above, but for ordinal data frames.

• A column named "cat_dist": Like above, but for categorical data frames.

• A column named "mix_dist": Like above, but for mixed-type (e.g., both categorical and discrete) data frames.

• One column for every input data frame in the corresponding data list which can either have the value of 0 or 1. The name of the column should be formatted as "inc_[]" where the square brackets are replaced with the name (as found in dl_summary(dl)$"name") of each data frame. When 0, that data frame will be excluded from that run of the SNF pipeline. When 1, that data frame will be included.

Usage

settings_df(
  dl,
  n_solutions = 0,
  min_removed_inputs = 0,
  max_removed_inputs = length(dl) - 1,
  dropout_dist = "exponential",
  min_alpha = NULL,
  max_alpha = NULL,
  min_k = NULL,
  max_k = NULL,
  min_t = NULL,
  max_t = NULL,
  alpha_values = NULL,
  k_values = NULL,
  t_values = NULL,
  possible_snf_schemes = c(1, 2, 3),
  clustering_algorithms = NULL,
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  dfl = NULL,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10,
  allow_duplicates = FALSE
)

Arguments

Value

A settings data frame

Description

This function calls the htShiny() function from the package InteractiveComplexHeatmap to assist users in identifying the indices of the boundaries between meta clusters in a meta cluster heatmap. By providing a heatmap of inter-solution similarities (obtained through meta_cluster_heatmap()), users can click on positions within the heatmap that appear to meaningfully separate major sets of similar cluster solutions by visual inspection. The corresponding indices of the clicked positions are printed to the console and also shown within the app. This function can only run from an interactive session of R.

Usage

shiny_annotator(ari_heatmap)

Arguments

Value

Does not return any value. Launches interactive shiny applet.

Examples

#dl <- data_list(
#    list(cort_sa, "cortical_surface_area", "neuroimaging", "continuous"),
#    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#    list(income, "household_income", "demographics", "continuous"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
#set.seed(42)
#my_sc <- snf_config(
#    dl = dl,
#    n_solutions = 20,
#    min_k = 20,
#    max_k = 50
#)
#
#sol_df <- batch_snf(dl, my_sc)
#
#sol_aris <- calc_aris(sol_df)
#
#meta_cluster_order <- get_matrix_order(sol_aris)
#
#ari_hm <- meta_cluster_heatmap(sol_aris, order = meta_cluster_order)
#
## Click on meta cluster boundaries to obtain `split_vec` values
#shiny_annotator(ari_hm)
#
#split_vec <- c(6, 10, 16)
#
#ari_hm <- meta_cluster_heatmap(
#    sol_aris,
#    order = meta_cluster_order,
#    split_vector = split_vec
#)

Description

Create or extract a sim_mats_list class object

Usage

sim_mats_list(x)

Arguments

Value

A sim_mats_list class object.

Description

Plot heatmap of similarity matrix

Usage

similarity_matrix_heatmap(
  similarity_matrix,
  order = NULL,
  cluster_solution = NULL,
  scale_diag = "mean",
  log_graph = TRUE,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  show_row_names = FALSE,
  show_column_names = FALSE,
  data = NULL,
  left_bar = NULL,
  right_bar = NULL,
  top_bar = NULL,
  bottom_bar = NULL,
  left_hm = NULL,
  right_hm = NULL,
  top_hm = NULL,
  bottom_hm = NULL,
  annotation_colours = NULL,
  min_colour = NULL,
  max_colour = NULL,
  split_vector = NULL,
  row_split = NULL,
  column_split = NULL,
  ...
)

Arguments

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the similarities between observations in the provided matrix.

Examples

#my_dl <- data_list(
#    list(
#        data = expression_df,
#        name = "expression_data",
#        domain = "gene_expression",
#        type = "continuous"
#    ),
#    list(
#        data = methylation_df,
#        name = "methylation_data",
#        domain = "gene_methylation",
#        type = "continuous"
#    ),
#    uid = "patient_id"
#)
#
#sc <- snf_config(my_dl, n_solutions = 10)
#
#sol_df <- batch_snf(my_dl, sc, return_sim_mats = TRUE)
#
#sim_mats <- sim_mats_list(sol_df)
#
#similarity_matrix_heatmap(
#    sim_mats[[1]],
#    cluster_solution = sol_df[1, ]
#)

Description

Squared (including weights) Euclidean distance

Usage

siw_euclidean_distance(df, weights_row)

Arguments

Value

distance_matrix A distance matrix.

Description

snf_config() constructs an SNF config object which inherits from classes snf_config and list. This object is used to store all settings required to transform data stored in a data_list class object into a space of cluster solutions by SNF. The SNF config object contains the following components: 1. A settings data frame (inherits from settings_df and data.frame). Data frame that stores SNF-specific hyperparameters and information about feature selection and weighting, SNF schemes, clustering algorithms, and distance metrics. Each row of the settings data frame corresponds to a distinct cluster solution. 2. A clustering algorithms list (inherits from clust_fns_list and list), which stores all clustering algorithms that the settings data frame can point to. 3. A distance metrics list (inherits from dist_metrics_list and list), which stores all distance metrics that the settings data frame can point to. 4. A weights matrix (inherits from weights_matrix, matrix, and array'), which stores the feature weights to use prior to distance calculations. Each column of the weights matrix corresponds to a different feature in the data list and each row corresponds to a different row in the settings data frame.

Usage

snf_config(
  dl = NULL,
  sdf = NULL,
  dfl = NULL,
  cfl = NULL,
  wm = NULL,
  n_solutions = 0,
  min_removed_inputs = 0,
  max_removed_inputs = length(dl) - 1,
  dropout_dist = "exponential",
  min_alpha = NULL,
  max_alpha = NULL,
  min_k = NULL,
  max_k = NULL,
  min_t = NULL,
  max_t = NULL,
  alpha_values = NULL,
  k_values = NULL,
  t_values = NULL,
  possible_snf_schemes = c(1, 2, 3),
  clustering_algorithms = NULL,
  continuous_distances = NULL,
  discrete_distances = NULL,
  ordinal_distances = NULL,
  categorical_distances = NULL,
  mixed_distances = NULL,
  snf_input_weights = NULL,
  snf_domain_weights = NULL,
  retry_limit = 10,
  cnt_dist_fns = NULL,
  dsc_dist_fns = NULL,
  ord_dist_fns = NULL,
  cat_dist_fns = NULL,
  mix_dist_fns = NULL,
  automatic_standard_normalize = FALSE,
  use_default_dist_fns = FALSE,
  clust_fns = NULL,
  use_default_clust_fns = FALSE,
  weights_fill = "ones"
)