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Heatmap of observation co-clustering across resampled data.

Source: R/coclustering.R
cocluster_heatmap.Rd

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

cocluster_df

A data frame containing coclustering data for a single cluster solution. This object is generated by the calculate_coclustering function.

cluster_rows

Argument passed to ComplexHeatmap::Heatmap().

cluster_columns

Argument passed to ComplexHeatmap::Heatmap().

show_row_names

Argument passed to ComplexHeatmap::Heatmap().

show_column_names

Argument passed to ComplexHeatmap::Heatmap().

dl

See ?similarity_matrix_heatmap.

data

See ?similarity_matrix_heatmap.

left_bar

See ?similarity_matrix_heatmap.

right_bar

See ?similarity_matrix_heatmap.

top_bar

See ?similarity_matrix_heatmap.

bottom_bar

See ?similarity_matrix_heatmap.

left_hm

See ?similarity_matrix_heatmap.

right_hm

See ?similarity_matrix_heatmap.

top_hm

See ?similarity_matrix_heatmap.

bottom_hm

See ?similarity_matrix_heatmap.

annotation_colours

See ?similarity_matrix_heatmap.

min_colour

See ?similarity_matrix_heatmap.

max_colour

See ?similarity_matrix_heatmap.

...

Arguments passed to ComplexHeatmap::Heatmap().

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"
#         )
#     )
# )