Visualize the correlation of layer modeling t-statistics with ComplexHeatmap

This function makes a ComplexHeatmap from the correlation matrix between a reference and query modeling statistics from layer_stat_cor(). For example, between the query statistics from a set of cell cluster/types derived from scRNA-seq or snRNA-seq data (among other types) and the reference layer statistics from the Human DLPFC Visium data (when using the default arguments).

layer_stat_cor_plot(
  cor_stats_layer,
  color_max = max(cor_stats_layer),
  color_min = min(cor_stats_layer),
  color_scale = RColorBrewer::brewer.pal(7, "PRGn"),
  query_colors = NULL,
  reference_colors = NULL,
  annotation = NULL,
  ...
)

Arguments

cor_stats_layer: The output of layer_stat_cor().
color_max: A numeric(1) specifying the highest correlation value for the color scale (should be between 0 and 1).
color_min: A numeric(1) specifying the lowest correlation value for the color scale (should be between 0 and -1).
color_scale: A character vector with three or more values specifying the color scale for the fill of the heatmap. The first value is used for color_min, the middle for zero, and the last for color_max. If an even number of colors are supplied, the last color is dropped to center zero.
query_colors: named character vector of colors, Adds colors to query row annotations.
reference_colors: named character vector of colors, Adds colors to reference column annotations.
annotation: annotation data.frame output of annotate_registered_clusters(), adds 'X' for good confidence annotations, '*' for poor confidence.
...: Additional parameters passed to ComplexHeatmap::Heatmap() such as cluster_rows and cluster_columns.

Value

(Heatmap-class) plot of t-stat correlations

Details

Includes functionality to add color annotations, (helpful to match to colors in Visium spot plots), and annotations from annotate_registered_clusters().

Author

Louise Huuki-Myers

Examples

## Obtain the necessary data
## reference human pilot modeling results
if (!exists("modeling_results")) {
    modeling_results <- fetch_data(type = "modeling_results")
}
#> 2025-05-09 16:31:04.120066 loading file /github/home/.cache/R/BiocFileCache/65ec6f775f8c_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1

## query spatialDLPFC modeling results
query_modeling_results <- fetch_data(
    type = "spatialDLPFC_Visium_modeling_results"
)
#> 2025-05-09 16:31:04.910012 loading file /github/home/.cache/R/BiocFileCache/71151741eec6_modeling_results_BayesSpace_k09.Rdata%3Fdl%3D1

## Compute the correlations
cor_stats_layer <- layer_stat_cor(
    stats = query_modeling_results$enrichment,
    modeling_results,
    model_type = "enrichment"
)

## Visualize the correlation matrix

## Default plot with no annotations and defaults for ComplexHeatmap()
layer_stat_cor_plot(cor_stats_layer)


## add Annotation colors
## add libd_layer_colors to reference Human Pilot layers
layer_stat_cor_plot(cor_stats_layer, reference_colors = libd_layer_colors)


## obtain colors for the query clusters
cluster_colors <- get_colors(clusters = rownames(cor_stats_layer))
layer_stat_cor_plot(cor_stats_layer,
    query_colors = cluster_colors,
    reference_colors = libd_layer_colors
)


## Apply additional ComplexHeatmap param
layer_stat_cor_plot(cor_stats_layer,
    cluster_rows = FALSE,
    cluster_columns = FALSE
)


## Add annotation
annotation_df <- annotate_registered_clusters(
    cor_stats_layer,
    confidence_threshold = .55
)
layer_stat_cor_plot(cor_stats_layer, annotation = annotation_df)


## change fill color scale
layer_stat_cor_plot(cor_stats_layer,
    color_scale = RColorBrewer::brewer.pal(2, "PiYG")
)
#> Warning: minimal value for n is 3, returning requested palette with 3 different levels


## All together
layer_stat_cor_plot(
    cor_stats_layer,
    color_scale = RColorBrewer::brewer.pal(5, "PiYG"),
    query_colors = cluster_colors,
    reference_colors = libd_layer_colors,
    annotation = annotation_df,
    cluster_rows = FALSE,
    cluster_columns = FALSE
)