---
title: "OrgHeatmap Tutorial"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{OrgHeatmap Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\VignetteDepends{OrgHeatmap}
---
``` r
library(OrgHeatmap)
#> Loading required package: ggplot2OrgHeatmap is an R package for visualizing numerical
data (such as gene expression, protein abundance, or physiological
indicators) on anatomical maps of human, mouse, and organelle
structures. It provides flexible customization options for color
schemes, organ system filtering, and quantitative comparisons.
# Install from local source
install.packages("OrgHeatmap_0.3.1.tar.gz", repos = NULL, type = "source")
# Or install from GitHub
# devtools::install_github("QiruiShen439/OrgHeatmap")
# Install dependencies if needed
install.packages(c("ggpolypath", "patchwork", "dplyr", "stringdist", "ggplot2", "sf", "viridis"))The package includes comprehensive example datasets:
# Load built-in example datasets
data_path <- system.file("extdata", "exampledata.Rdata", package = "OrgHeatmap")
load(data_path)
# Check available datasets
ls(pattern = "example_Data")#> Example Data 1 (Mouse):
#> organ value
#> 10 lymph_nodes 15.7
#> 9 lung 15.6
#> 5 uterus 14.0
#> 17 adrenal_gland 13.7
#> 25 tongue 13.6
#> 18 stomach 13.1
#>
#> Example Data 3 (Human):
#> organ value
#> 10 lymph_nodes 0.029
#> 9 lung 0.041
#> 5 uterus 0.026
#> 17 adrenal_gland 0.014
#> 25 tongue 0.038
#> 18 stomach 0.050# Basic visualization with default parameters
result <- OrgHeatmap(data = example_Data3)
print(result$plot)The function returns a comprehensive list with multiple components:
# Explore returned results
names(result)Key components: - plot: Visualization object (ggplot2) -
clean_data: Processed data with standardized organ names -
system_used: Organ system used for filtering -
mapped_organs: Successfully mapped organ names -
missing_organs: Organs without coordinate data -
total_value: Sum of all values
# Human visualization (default)
human_result <- OrgHeatmap(example_Data3, title = "Human Organ Visualization")
# Mouse visualization
mouse_result <- OrgHeatmap(
data = example_Data1,
species = "mouse",
system = "digestive",
title = "Mouse Digestive System"
)
# Organelle visualization
organelle_data <- data.frame(
organ = c("mitochondrion", "nucleus", "endoplasmic_reticulum"),
value = c(15.2, 8.7, 6.3)
)
organelle_result <- OrgHeatmap(
data = organelle_data,
species = "organelle",
title = "Organelle Expression"
)# Filter by specific organ system
circulatory_plot <- OrgHeatmap(
data = example_Data3,
system = "circulatory",
title = "Circulatory System Data"
)
# View available systems for human
unique(human_organ_systems$system)# Unified color scheme with palette
palette_plot <- OrgHeatmap(
data = example_Data3,
palette = "PuBuGn",
reverse_palette = TRUE,
title = "PuBuGn Palette (Reversed)"
)# Custom 2-color gradient
custom_2color <- OrgHeatmap(
data = example_Data3,
color_low = "#FFE4E1",
color_high = "#DC143C",
title = "Custom 2-Color Gradient"
)
# Custom 3-color gradient
custom_3color <- OrgHeatmap(
data = example_Data3,
color_low = "#FF0000",
color_mid = "#FFFF00",
color_high = "#0000FF",
title = "Custom 3-Color Gradient"
)# Different colors for heatmap and bar chart
separate_colors <- OrgHeatmap(
data = example_Data3,
color_low = "#F7FBFF",
color_high = "#08306B",
organbar_low = "#FFF7BC",
organbar_high = "#D95F0E",
organbar = TRUE,
title = "Separate Heatmap/Bar Colors"
)# Enable bar chart with customization
bar_chart_plot <- OrgHeatmap(
data = example_Data3,
organbar = TRUE,
organbar_title = "Expression Level",
organbar_digit = 2,
organbar_color = "skyblue", # Solid color for bars
title = "Custom Bar Chart"
)# Handle non-standard organ names
custom_mapping <- c(
"adrenal" = "adrenal_gland",
"lymph node" = "lymph_node",
"soft tissue" = "muscle"
)
mapped_result <- OrgHeatmap(
data = example_Data3,
organ_name_mapping = custom_mapping,
title = "Custom Organ Name Mapping"
)# Example data with duplicate organs
dup_data <- data.frame(
organ = c("heart", "heart", "liver", "brain", "brain", "brain"),
value = c(10, 20, 15, 5, 10, 15)
)
# Mean aggregation
mean_result <- OrgHeatmap(
data = dup_data,
aggregate_method = "mean",
title = "Mean Aggregation"
)
# Sum aggregation
sum_result <- OrgHeatmap(
data = dup_data,
aggregate_method = "sum",
title = "Sum Aggregation"
)# Show all organ outlines for anatomical context
showall_plot <- OrgHeatmap(
data = example_Data3,
showall = TRUE,
fillcolor_other = "#D3D3D3",
title = "All Organ Outlines Displayed"
)
# Hide body outline
no_outline <- OrgHeatmap(
data = example_Data3,
outline = FALSE,
title = "Without Body Outline"
)# Extend default organ system mapping
custom_system_map <- rbind(
human_organ_systems,
data.frame(
organ = c("prostate", "custom_organ"),
system = c("reproductive", "custom_system"),
stringsAsFactors = FALSE
)
)
custom_system_result <- OrgHeatmap(
data = example_Data3,
organ_system_map = custom_system_map,
system = "custom_system",
title = "Custom Organ System"
)# Data with custom column names
custom_data <- data.frame(
organ_name = c("heart", "liver", "brain"),
expression_level = c(15, 25, 20)
)
custom_col_result <- OrgHeatmap(
data = custom_data,
organ_col = "organ_name",
value_col = "expression_level",
title = "Custom Column Names"
)# Save plot and cleaned data
save_result <- OrgHeatmap(
data = example_Data3,
save_plot = TRUE,
plot_path = file.path(tempdir(), "my_visualization.png"),
plot_width = 12,
plot_height = 10,
plot_dpi = 300,
save_clean_data = TRUE,
clean_data_path = file.path(tempdir(), "cleaned_data.rds"),
title = "Saved Visualization"
)
# Access saved files
list.files(path = tempdir(),pattern = "my_visualization|cleaned_data")The package uses a unified color system with clear priority levels:
organbar_low/organbar_high (bar chart
colors)color_low/color_high/color_mid
(heatmap colors)palette with optional
reverse_palette# organbar colors take precedence
priority_example <- OrgHeatmap(
data = example_Data3,
organbar_low = "blue", # Highest priority
color_low = "red", # Overridden by organbar_low
palette = "YlOrRd", # Lowest priority
organbar = TRUE,
title = "Color Priority Example"
)# Check available organs for each species
cat("Human organs:", names(human_organ_coord), "\n")
cat("Mouse organs:", names(mouse_organ_coord), "\n")
cat("Organelle organelles:", names(organelle_organ_coord), "\n")
# Filter valid organs only
valid_result <- OrgHeatmap(
data = example_Data3,
valid_organs = c("heart", "liver", "brain", "kidney")
)# Validate RColorBrewer palettes
RColorBrewer::brewer.pal.info
# Valid viridis options
c("viridis", "plasma", "magma", "inferno", "cividis")# Install all dependencies
install.packages(c("sf", "ggpolypath", "patchwork", "stringdist", "viridis"))
# Check package loading
library(OrgHeatmap)
packageVersion("OrgHeatmap")# Use system filtering to reduce rendering time
filtered_plot <- OrgHeatmap(
data = large_dataset,
system = "specific_system",
showall = FALSE
)# For publication-quality figures
publication_plot <- OrgHeatmap(
data = example_Data3,
save_plot = TRUE,
plot_path = file.path(tempdir(), "publication_figure.tiff"),
plot_width = 8,
plot_height = 6,
plot_dpi = 600,
plot_device = "tiff"
)OrgHeatmap provides comprehensive tools for biological
data visualization with the following key features:
| Feature | Key Parameters |
|---|---|
| Multi-species | species = "human"/"mouse"/"organelle" |
| System filtering | system, organ_system_map |
| Color configuration | palette, color_low/color_high,
organbar_low/organbar_high |
| Bar charts | organbar, organbar_title,
organbar_color |
| Name standardization | organ_name_mapping |
| Data aggregation | aggregate_method = "mean"/"sum"/"count" |
| Output control | save_plot, save_clean_data |
For complete parameter documentation: ?OrgHeatmap
The package is designed to handle real-world biological data challenges including non-standard organ names, duplicate entries, and flexible visualization requirements across multiple species and subcellular structures.