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

Dual Information Display: Shows both expression intensity and percentage in a single visualization
Gene Grouping: Organize genes by functional categories with color-coded annotations
Time Course Support: Handle time series data with proper ordering and visualization
Cell Type Annotations: Annotate and group different cell types
Flexible Display Options: Show/hide different annotation layers (gene groups, time points, cell types)
Highly Customizable: Extensive parameters for colors, sizes, fonts, and layouts
Seurat Integration: Direct integration with Seurat objects
Enhanced Color Support: Compatible with multiple R color packages (RColorBrewer, ggsci, viridis, custom colors)
Customizable Annotations: User-defined titles for gene groups, time points, and cell types
Flexible Visual Control: Optional cell borders and column annotations
Gene Name Mapping: Custom display names for genes on y-axis

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

seurat_object - A Seurat object containing single cell data
features - Character vector of gene names to plot

Data Organization Parameters

gene_classification - Named list where names are group labels and values are character vectors of gene names (default: NULL for no gene grouping)
group_by - Character string specifying the metadata column to group by (default: "seurat_clusters")
idents - Numeric or character vector specifying which cell groups to include (default: NULL for all)

Order Control Parameters

time_points_order - Character vector specifying order of time points (default: NULL for automatic)
cell_types_order - Character vector specifying order of cell types (default: NULL for automatic)

Color and Style Parameters

color_range - Numeric vector of length 3 or 4 specifying color mapping range (default: c(-1, 0, 2))
color_palette - Function or character vector specifying colors (default: viridis palette)
max_circle_size - Numeric specifying maximum circle radius in mm (default: 2)

Font Parameters

row_fontsize - Numeric specifying row name font size (default: 8)
col_fontsize - Numeric specifying column name font size (default: 9)
col_name_rotation - Numeric specifying column name rotation angle (default: 90)
row_title_fontsize - Numeric specifying row title font size (default: 10)
col_title_fontsize - Numeric specifying column title font size (default: 10)

Legend Parameters

show_heatmap_legend - Logical indicating whether to show heatmap legend (default: TRUE)
show_percentage_legend - Logical indicating whether to show percentage legend (default: TRUE)
legend_side - Character string specifying legend position (default: "right")
merge_legends - Logical indicating whether to merge legends (default: TRUE)
percentage_legend_title - Character string for percentage legend title (default: "Expression %")
percentage_legend_labels - Character vector for percentage legend labels (default: c("0%", "25%", "50%", "75%", "100%"))

Visual Appearance Parameters

cell_border_color - Character string specifying cell border color (default: "grey80")
show_cell_borders - NEW: Logical indicating whether to show cell border lines (default: TRUE)

Data Parsing Parameters

split_pattern - Character string used to split column names for parsing (default: "_")

Color Palette Parameters

gene_color_palette - ENHANCED: Character string specifying palette name OR character vector of colors for gene groups (default: "Set1")
time_color_palette - ENHANCED: Character string specifying palette name OR character vector of colors for time points (default: "Accent")
celltype_color_palette - ENHANCED: Character string specifying palette name OR character vector of colors for cell types (default: "Dark2")

Display Control Parameters

show_gene_grouping - Logical indicating whether to show gene grouping (default: TRUE if gene_classification provided)
show_time_annotation - Logical indicating whether to show time point annotation (default: TRUE)
show_celltype_annotation - Logical indicating whether to show cell type annotation (default: TRUE)
show_column_annotation - NEW: Logical indicating whether to show column annotations (default: TRUE)
split_by - Character string specifying how to split columns: "time", "celltype", or "none" (default: "time")

Customization Parameters (NEW)

gene_group_title - Character string for gene group annotation title (default: "Gene Group")
time_point_title - Character string for time point annotation title (default: "Time Point")
cell_type_title - Character string for cell type annotation title (default: "Cell Type")
gene_name_mapping - Named character vector for mapping gene names, where names are original gene names and values are display names (default: NULL)

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

`seurat_object`: A Seurat object containing single cell data. (No default)
`features`: Character vector of gene names to plot. (No default)
`gene_classification`: Named list where names are group labels and values are character vectors of gene names. (Default: NULL)
`group_by`: Character string specifying the metadata column to group by. (Default: "seurat_clusters")
`idents`: Numeric or character vector specifying which cell groups to include. (Default: NULL for all)
`time_points_order`: Character vector specifying order of time points. (Default: NULL for automatic ordering)
`cell_types_order`: Character vector specifying order of cell types. (Default: NULL for automatic ordering)
`color_range`: Numeric vector of length 3 or 4 specifying color mapping range for expression values. (Default: c(-1, 0, 2))
`color_palette`: Function or character vector specifying colors for the expression heatmap. (Default: NULL, then uses viridis palette if available, otherwise color_palette_main)
`max_circle_size`: Numeric specifying maximum circle radius in mm for percentage representation. (Default: 2)
`row_fontsize`: Numeric specifying row name font size. (Default: 8)
`col_fontsize`: Numeric specifying column name font size. (Default: 9)
`col_name_rotation`: Numeric specifying column name rotation angle. (Default: 90)
`row_title_fontsize`: Numeric specifying row title font size (for gene groups). (Default: 10)
`col_title_fontsize`: Numeric specifying column title font size (for time/cell type splits). (Default: 10)
`show_heatmap_legend`: Logical indicating whether to show heatmap legend for expression values. (Default: TRUE)
`show_percentage_legend`: Logical indicating whether to show percentage legend for circle sizes. (Default: TRUE)
`legend_side`: Character string specifying legend position ("left", "right", "top", "bottom"). (Default: "right")
`cell_border_color`: Character string specifying cell border color. Use NA for no border. (Default: "grey80")
`split_pattern`: Character string used to split column names for parsing time points and cell types. (Default: "_" )
`gene_color_palette`: ENHANCED: Character string specifying palette name OR character vector of colors for gene groups. (Default: "Set1")
`time_color_palette`: ENHANCED: Character string specifying palette name OR character vector of colors for time points. (Default: "Accent")
`celltype_color_palette`: ENHANCED: Character string specifying palette name OR character vector of colors for cell types. (Default: "Dark2")
`show_gene_grouping`: Logical indicating whether to show gene grouping annotation. (Default: NULL, resolves to TRUE if gene_classification is provided, else FALSE)
`show_time_annotation`: Logical indicating whether to show time point annotation. (Default: TRUE)
`show_celltype_annotation`: Logical indicating whether to show cell type annotation. (Default: TRUE)
`split_by`: Character string specifying how to split columns: "time", "celltype", or "none". (Default: "time")
`merge_legends`: Logical indicating whether to merge legends if possible. (Default: TRUE)
`percentage_legend_title`: Character string for percentage legend title. (Default: "Expression %")
`percentage_legend_labels`: Character vector for percentage legend labels. (Default: c("0%", "25%", "50%", "75%", "100%"))
`return_data`: Logical indicating whether to return underlying data along with the heatmap object. (Default: FALSE)
`save_plot`: Character string specifying file path to save plot (e.g., "my_heatmap.png"). (Default: NULL)
`plot_width`: Numeric specifying plot width in inches when saving. (Default: 10)
`plot_height`: Numeric specifying plot height in inches when saving. (Default: 8)
`plot_dpi`: Numeric specifying plot resolution in DPI when saving. (Default: 300)

New Customization Parameters

`gene_group_title`: NEW: Character string for gene group annotation title. (Default: "Gene Group")
`time_point_title`: NEW: Character string for time point annotation title. (Default: "Time Point")
`cell_type_title`: NEW: Character string for cell type annotation title. (Default: "Cell Type")
`show_cell_borders`: NEW: Logical indicating whether to show cell border lines. (Default: TRUE)
`show_column_annotation`: NEW: Logical indicating whether to show column annotations. (Default: TRUE)
`gene_name_mapping`: NEW: Named character vector for mapping gene names, where names are original gene names and values are display names. (Default: NULL)

Data Source Control

`assay`: Character string specifying which assay to use from Seurat object. (Default: NULL, uses active assay)
`slot`: Character string specifying which slot to extract from assay (e.g., 'scale.data', 'data', 'counts'). (Default: 'scale.data')

Clustering Control

`cluster_cells`: Logical indicating whether to cluster cells/columns. (Default: TRUE)
`cluster_features`: Logical indicating whether to cluster features/rows. (Default: TRUE)
`clustering_distance_rows`: Character string specifying distance method for row clustering (e.g., "euclidean", "pearson"). (Default: "euclidean")
`clustering_distance_cols`: Character string specifying distance method for column clustering. (Default: "euclidean")
`clustering_method_rows`: Character string specifying clustering method for rows (e.g., "complete", "ward.D2"). (Default: "complete")
`clustering_method_cols`: Character string specifying clustering method for columns. (Default: "complete")

Color Mapping Control

`color_palette_main`: Character vector of 3 colors for main heatmap gradient (used if color_palette is NULL and viridis is not available). (Default: c("blue", "white", "red"))
`annotation_colors`: Named list specifying custom colors for annotations (e.g., list(CellType = c(TypeA = "red"))). (Default: NULL)

Label and Legend Control

`show_feature_names`: Logical indicating whether to show row names (gene names). (Default: TRUE)
`feature_names_gp`: gpar object for controlling feature name appearance. (Default: NULL, then gpar(fontsize = row_fontsize))
`legend_title`: Character string for main heatmap legend title. (Default: "Expression")

Other Parameters

`...`: Additional parameters passed to ComplexHeatmap::Heatmap() for maximum customization.

Available Color Palettes

RColorBrewer Palettes

Qualitative: "Set1", "Set2", "Set3", "Pastel1", "Pastel2", "Paired", "Dark2", "Accent"
Sequential: "Blues", "Reds", "Greens", "Purples", "Oranges", "Greys"
Diverging: "RdYlBu", "RdBu", "PiYG", "PRGn", "BrBG", "PuOr", "RdGy", "Spectral"

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:

Time + Cell Type: "timepoint_celltype" (e.g., "Mock_Epidermis", "24hpi_Guard_cell")
Complex naming: "timepoint_celltype_additional_info" (e.g., "Mock_Guard_cell_cluster1")

The package automatically parses:

Position 1: Time point
Position 2+: Cell type (can include underscores)

Requirements

R (>= 4.0.0)
ComplexHeatmap
Seurat
dplyr
tidyr
RColorBrewer
circlize
grid

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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README.md		README.md

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SingleCellComplexHeatMap

Features

Installation

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()

create_gene_annotations()

create_cell_annotations()

Data Preparation

Required Data Format

Usage Examples

Basic Usage with All Default Parameters

Complete Parameter Customization

Different Use Cases

1. Full Analysis (Time + Cell Type + Gene Groups)

2. Cell Type Only Analysis

3. Simple Expression Analysis (No Grouping)

4. Custom Color Schemes

5. Large Dataset Optimization

6. Publication-Ready Styling

Parameters

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

Step-by-Step Workflow

Column Naming Convention

Requirements

License

Citation

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Main Function: `create_single_cell_complex_heatmap()`

`prepare_expression_matrices()`

`create_gene_annotations()`

`create_cell_annotations()`

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