alluvial_cluster_plot: Alluvial plot of patients across cluster counts and important...

In metasnf: Meta Clustering with Similarity Network Fusion

Alluvial plot of patients across cluster counts and important features

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

cluster_sequence	A list of clustering algorithms.
similarity_matrix	A similarity matrix.
dl	A data list.
data	A data frame that contains any features to include in the plot.
key_outcome	The name of the feature that determines how each patient stream is coloured in the alluvial plot.
key_label	Name of key outcome to be used for the plot legend.
extra_outcomes	Names of additional features to add to the plot.
title	Title of the plot.

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"
)

metasnf documentation built on April 3, 2025, 5:40 p.m.