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Generate input files to be used for pheatmap from the mmo object

GenerateHeatmapInputs.Rd

This function generates heatmap inputs from the mmo object, including fold change or mean values, distance matrix, and row labels for custom-annotated features.

Usage

GenerateHeatmapInputs(
  mmo,
  filter_feature = FALSE,
  feature_list = NULL,
  filter_group = FALSE,
  group_list = NULL,
  summarize = "mean",
  control_group = "ctrl",
  normalization = "None",
  distance = "dreams"
)

Arguments

mmo

The mmo object with sirius annotation and normalized data

filter_feature

Boolean to filter features by feature_list (default: FALSE)

feature_list

A vector of feature names to filter (default: NULL)

filter_group

Boolean to filter groups by group_list (default: FALSE)

group_list

A vector of group names to filter (default: NULL)

summarize

The summarization method to use. Options are 'fold_change' or 'mean' (default: 'mean')

control_group

The group to use as control for fold change calculation (default: 'ctrl')

normalization

The normalization method to use. Options are 'None', 'Log', 'Meancentered', or 'Z'

distance

The distance metric to use. Options are 'dreams', 'cosine', or 'm2ds' (default: 'dreams')

Value

A list containing the following elements:

  • FC_matrix: A matrix of fold change or mean values

  • dist_matrix: A distance matrix based on the specified distance metric

  • row_label: A vector of row labels for custom-annotated features (See AddCustomAnnot()). If no custom annotation is available, feature IDs are used.

  • heatmap_data: A data frame containing the heatmap data with feature IDs and values

Examples

if (FALSE) {
# Generate heatmap inputs to visualize fold change values with log normalization and dreams distance
heatmap_inputs <- GenerateHeatmapInputs(
 mmo, summarize = 'fold_change', control_group = 'Control', 
 normalization = 'None', distance = 'dreams'
)
# Generate heatmap inputs to visualize mean values
heatmap_inputs <- GenerateHeatmapInputs(
 mmo, summarize = 'mean', normalization = 'None', distance = 'dreams'
)
# The resulting list contains FC_matrix, dist_matrix, row_label, and heatmap_data
# A heatmap can be generated using pheatmap
# 'clustering_distance_rows' option make the dendrogram follows chemical distances of features. 
#  -Delete this option to visualize the heatmap following cannonical clustering
pheatmap(mat = heatmap_inputs$FC_matrix, 
    cluster_rows = TRUE, #do not change
    clustering_distance_rows = heatmap_inputs$dist_matrix, 
    cluster_cols = TRUE, 
    clustering_method = "average", #UPGMA
    show_rownames = TRUE, 
    show_colnames = TRUE,
    cellwidth = 25,
    cellheight = 0.05,
    treeheight_row = 100,
    fontsize_row = 3,
    fontsize_col = 15,
    scale = 'none',
    annotation_names_row = TRUE,
    labels_row = heatmap_inputs$row_label,
    )
}