---
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.
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
#>
#> Example Data 4 (Organelle):
#> organ value
#> 1 cell_membrane 11.2
#> 2 cytoplasm 3.2
#> 3 endoplasmic_reticulum 4.5
#> 4 golgi_apparatus 8.9
#> 5 lysosome 20.1
#> 6 mitochondrion 7.7The function returns a comprehensive list with multiple components:
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 using the built-in example_Data4
organelle_result <- OrgHeatmap(
data = example_Data4,
species = "organelle",
title = "Organelle Expression"
)# 1. Filter by a single organ system
circulatory_plot <- OrgHeatmap(
data = example_Data3,
system = "circulatory",
title = "Circulatory System Data"
)
# 2. Filter by MULTIPLE organ systems simultaneously
multi_system_plot <- OrgHeatmap(
data = example_Data3,
system = c("digestive", "respiratory"),
title = "Digestive & Respiratory Systems"
)
# View all available systems for human
unique(human_organ_systems$system)# 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"
)# 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"
)# 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# 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")
)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 (supports multiple, e.g.,
c("digestive", "immune")),
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.