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Showing content from http://cran.rstudio.com/web/packages/RColorBrewer/../SingleCellComplexHeatMap/readme/README.html below:

README

SingleCellComplexHeatMap

A R package for creating complex heatmaps for single cell RNA-seq data that simultaneously display gene expression levels (as color intensity) and expression percentages (as circle sizes).

Features Installation 
# Install from GitHub (when available)
# devtools::install_github("xuecheng328/SingleCellComplexHeatMap")

# For now, install locally
devtools::install_local("/path/to/SingleCellComplexHeatMap")
Function Parameters Main Function: create_single_cell_complex_heatmap() Required Parameters Data Organization Parameters Order Control Parameters Color and Style Parameters Font Parameters Legend Parameters Visual Appearance Parameters Data Parsing Parameters Color Palette Parameters Display Control Parameters Customization Parameters (NEW) Helper Functions prepare_expression_matrices() 
prepare_expression_matrices(
  seurat_object,           # Required: Seurat object
  features,                # Required: Gene names
  group_by = "seurat_clusters",
  idents = NULL,
  split_pattern = "_",
  time_position = 1,
  celltype_start = 2
)
create_gene_annotations() 
create_gene_annotations(
  exp_mat,                 # Required: Expression matrix
  percent_mat,             # Required: Percentage matrix
  gene_classification,     # Required: Gene groups
  color_palette = "Set1",  # ENHANCED: Now supports color vectors
  sort_within_groups = TRUE,
  annotation_title = "Gene Group"  # NEW: Custom title
)
create_cell_annotations() 
create_cell_annotations(
  exp_mat,                 # Required: Expression matrix
  percent_mat,             # Required: Percentage matrix
  split_pattern = "_",
  time_position = 1,
  celltype_start = 2,
  time_points_order = NULL,
  cell_types_order = NULL,
  time_color_palette = "Accent",    # ENHANCED: Now supports color vectors
  celltype_color_palette = "Dark2", # ENHANCED: Now supports color vectors
  show_time_annotation = TRUE,
  show_celltype_annotation = TRUE,
  time_point_title = "Time Point",  # NEW: Custom title
  cell_type_title = "Cell Type"     # NEW: Custom title
)
Data Preparation Required Data Format

Important: This package expects your group identifiers to follow the format "timepoint_celltype" when you want to display both time and cell type information.

library(dplyr)

# Method 1: Create combined group column (recommended for time course + cell type analysis)
seurat_obj@meta.data <- seurat_obj@meta.data %>%
  mutate(group = paste(timepoint, celltype, sep = "_"))

# Example result: "Mock_Epidermis", "24hpi_Guard_cell", etc.

# Method 2: Use existing metadata columns for simpler analysis
# If you only want cell type information:
# group_by = "celltype"
# If you only want cluster information:
# group_by = "seurat_clusters"
Usage Examples Basic Usage with All Default Parameters 
library(SingleCellComplexHeatMap)

# Minimal usage
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = gene_list
)
Complete Parameter Customization 
# Full customization example
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = gene_list,
  gene_classification = gene_groups,
  group_by = "group",
  idents = c(9, 10, 13),
  time_points_order = c("Mock", "24hpi", "48hpi", "72hpi"),
  cell_types_order = c("Epidermis", "Guard_cell", "Mesophyll"),
  color_range = c(-2, 0, 3),
  color_palette = c("blue", "white", "red"),
  max_circle_size = 3,
  row_fontsize = 10,
  col_fontsize = 8,
  col_name_rotation = 45,
  row_title_fontsize = 12,
  col_title_fontsize = 12,
  show_heatmap_legend = TRUE,
  show_percentage_legend = TRUE,
  legend_side = "bottom",
  merge_legends = FALSE,
  percentage_legend_title = "% Expressing Cells",
  percentage_legend_labels = c("0", "20", "40", "60", "80", "100"),
  cell_border_color = "white",
  split_pattern = "_",
  gene_color_palette = "Dark2",
  time_color_palette = "Set3",
  celltype_color_palette = "Paired",
  show_gene_grouping = TRUE,
  show_time_annotation = TRUE,
  show_celltype_annotation = TRUE,
  split_by = "celltype",
  gene_group_title = "Gene Categories",
  time_point_title = "Treatment Time",
  cell_type_title = "Cell Types",
  show_cell_borders = TRUE,
  show_column_annotation = TRUE,
  gene_name_mapping = NULL
)
Different Use Cases 1. Full Analysis (Time + Cell Type + Gene Groups) 
# Prepare data with combined group
seurat_obj@meta.data <- seurat_obj@meta.data %>%
  mutate(group = paste(sample, Cell_type, sep = "_"))

gene_groups <- list(
  "Senescence" = c("Gene1", "Gene2", "Gene3"),
  "Stress_Response" = c("Gene4", "Gene5", "Gene6")
)

heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5", "Gene6"),
  gene_classification = gene_groups,
  group_by = "group",
  time_points_order = c("Mock", "24hpi", "48hpi", "72hpi"),
  cell_types_order = c("Cell_cycle", "Companion_cell", "Epidermis")
)
2. Cell Type Only Analysis 
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = your_genes,
  group_by = "celltype",  # Use existing celltype column
  show_time_annotation = FALSE,
  split_by = "none"
)
3. Simple Expression Analysis (No Grouping) 
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = your_genes,
  gene_classification = NULL,  # No gene grouping
  group_by = "seurat_clusters",
  show_time_annotation = FALSE,
  show_celltype_annotation = FALSE,
  split_by = "none"
)
4. Custom Color Schemes 
# Using different color palettes
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = your_genes,
  gene_classification = gene_groups,
  color_range = c(-1.5, 0, 1.5, 3),  # 4-point color range
  color_palette = c("darkblue", "blue", "white", "red", "darkred"),
  gene_color_palette = "Spectral",
  time_color_palette = "Set2",
  celltype_color_palette = "Pastel1"
)
5. Large Dataset Optimization 
# For large datasets, reduce visual complexity
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = your_genes,
  max_circle_size = 1.5,  # Smaller circles
  row_fontsize = 6,       # Smaller font
  col_fontsize = 6,
  cell_border_color = NA, # No borders for cleaner look
  merge_legends = TRUE    # Compact legends
)
6. Publication-Ready Styling 
# High-quality publication figure
heatmap <- create_single_cell_complex_heatmap(
  seurat_object = seurat_obj,
  features = your_genes,
  gene_classification = gene_groups,
  color_range = c(-2, 0, 2),
  color_palette = c("#2166AC", "#F7F7F7", "#B2182B"),  # Custom colors
  max_circle_size = 2.5,
  row_fontsize = 10,
  col_fontsize = 10,
  row_title_fontsize = 12,
  col_title_fontsize = 12,
  percentage_legend_title = "Fraction of cells",
  percentage_legend_labels = c("0", "0.25", "0.50", "0.75", "1.0"),
  legend_side = "right",
  merge_legends = TRUE,
  gene_group_title = "Functional Categories",
  time_point_title = "Time Points",
  cell_type_title = "Cell Types",
  show_cell_borders = TRUE,
  cell_border_color = "white"
)
Parameters

This section details all parameters for the create_single_cell_complex_heatmap function.

Main Parameters New Customization Parameters Data Source Control Clustering Control Color Mapping Control Label and Legend Control Other Parameters Available Color Palettes RColorBrewer Palettes Custom Color Examples 
# Viridis colors
color_palette = viridis::viridis(3)

# Custom RGB colors
color_palette = c("#440154", "#21908C", "#FDE725")

# Named colors
color_palette = c("navy", "white", "firebrick")
Step-by-Step Workflow 
# Step 1: Prepare matrices
matrices <- prepare_expression_matrices(
  seurat_object = seurat_obj,
  features = your_genes,
  group_by = "group",
  idents = c(9, 10, 13)  # Specific cell clusters
)

# Step 2: Create gene annotations (if needed)
if (!is.null(gene_groups)) {
  gene_ann <- create_gene_annotations(
    exp_mat = matrices$exp_mat,
    percent_mat = matrices$percent_mat,
    gene_classification = gene_groups
  )
}

# Step 3: Create cell annotations
cell_ann <- create_cell_annotations(
  exp_mat = gene_ann$exp_mat_ordered,
  percent_mat = gene_ann$percent_mat_ordered,
  time_points_order = c("Mock", "24hpi", "48hpi", "72hpi"),
  cell_types_order = c("Epidermis", "Guard_cell", "Mesophyll")
)
Column Naming Convention

For optimal functionality, ensure your column identifiers follow these patterns:

The package automatically parses: - Position 1: Time point - Position 2+: Cell type (can include underscores)

Requirements License

MIT License - see LICENSE file for details.

Citation

If you use this package in your research, please cite:

SingleCellComplexHeatMap: Complex Heatmaps for Single Cell Expression Data
XueCheng (2024)

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