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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 co-clustering 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

# \donttest{
    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"
    )
#> ℹ 175 observations dropped due to incomplete data.
    
    sc <- snf_config(my_dl, n_solutions = 5, max_k = 40)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.
    
    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
    )
    
    coclustering_results <- calculate_coclustering(
        batch_subsample_results, 
        sol_df,
        verbose = TRUE
    )
#> Processing solution 1/5
#> Processing solution 2/5
#> Processing solution 3/5
#> Processing solution 4/5
#> Processing solution 5/5
    
    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"
            )
        )
    )

# }