In drieslab/Giotto_site_suite: Spatial Single-Cell Transcriptomics Toolbox
How to create a Giotto Object
In this tutorial, the methodology and syntax to create a giottoObject is shown and osmFISH data is used throughout the tutorial.
Import Giotto and Download the Data
To download this data, please ensure that wget is installed locally.
library(Giotto)
genv_exists = checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment.
installGiottoEnvironment()
}
# Specify path from which data may be retrieved/stored
data_directory = paste0(getwd(),'/gobject_data/')
# alternatively, "/path/to/where/the/data/lives/"
# Specify path to which results may be saved
results_directory = paste0(getwd(),'/gobject_results/')
# alternatively, "/path/to/store/the/results/"
# Download osmFISH dataset to data_directory
getSpatialDataset(dataset = 'osmfish_SS_cortex', directory = data_directory, method = 'wget')
Minimum requirements for a Giotto Object
- Expression matrix
- Spatial locations (unnecessary for scRNAseq analysis)
Here, creating a giottoObject with the minimum requirements is shown in two examples.
Data formatting guidelines are shown below this code block.
# Example 1.
# Create a Giotto object using data directly from file paths
osm_exprs = paste0(data_directory, "osmFISH_prep_expression.txt")
osm_locs = paste0(data_directory, "osmFISH_prep_cell_coordinates.txt")
minimum_gobject1 = createGiottoObject(expression = osm_exprs,
spatial_locs = osm_locs)
# Example 2.
# Create a Giotto object using objects already loaded into workspace
expression_matrix = readExprMatrix(path = osm_exprs) # fast method to read expression matrix
cell_locations = data.table::fread(file = osm_locs)
minimum_gobject2 = createGiottoObject(expression = expression_matrix,
spatial_locs = cell_locations)
Expression file formatting
Expression count matrices accepted by Giotto should have columns correlating to individual cells (cell IDs) and rows to individual features (feature IDs).
data.table objects and text-based files ie: (.csv, .tsv, .txt) to be read in should have column 1 be feature IDs.
| |feat_ID| Cell_1| Cell_2| Cell_3| ...|
|-|:------|-------|-------|-------|----|
|1|Gene1 | | | | |
|2|Gene2 | | | | |
|3|Gene3 | | | | |
| |... | | | | |
matrix and DelayedMatrix objects should have feature IDs already incorporated as the rownames.
| | Cell_1| Cell_2| Cell_3| ...|
|:------|-------|-------|-------|----|
|Gene1 | | | | |
|Gene2 | | | | |
|Gene3 | | | | |
|... | | | | |
Locations file formatting
Numerical columns will be interpreted in order of x, y, and (optionally) z coordinate. The first non-numerical column will be taken as cell IDs
| | sdimx| sdimy| sdimz| cell_ID|
|-|------|------|------|--------|
|1| | | | |
|2| | | | |
|3| | | | |
| | | | | |
*Note: multiple expression files during can be given at once during Giotto object creation by using a named list.
# Arbitrary modifications
scaled_matrix = expression_matrix * 1.2
custom_matrix = expression_matrix * 0.5
# Provide multiple expression matrices at once to the Giotto Object
# If these matrices are stored in files rather than in the workspace,
# file paths may be provided instead of variables
multi_expr_gobject = createGiottoObject(expression = list(raw = expression_matrix,
scaled = scaled_matrix,
custom = custom_matrix),
spatial_locs = cell_locations)
Customizing the Giotto Object
By providing values to other createGiottoObject parameters, it is possible to add:
-
Cell or feature (gene) metadata: see addCellMetadata and addFeatMetadata
-
Spatial networks or grids: see Visualizations
-
Dimension reduction: see Clustering
-
Images: see Imaging
-
giottoInstructions: see createGiottoInstructions and below
Providing giottoInstructions allows the specification of:
- An alternative python path if using the Giotto Environment (default) is not desired
- A directory to which resulting plots will save
- Plot formatting
There are four functions to utilize when setting instructions:
- createGiottoInstructions: creates instructions that are provided to createGiottoObject
- showGiottoInstructions: displays the instructions of a Giotto object
- changeGiottoInstructions: replaces 1 or more of the instruction parameters (e.g. plotting format)
- replaceGiottoInstructions: replaces all instructions with new instructions (e.g after subsetting)
Here is an example of a more customized Giotto object.
# Specify data with file paths
osm_exprs = paste0(data_directory, "osmFISH_prep_expression.txt")
osm_locs = paste0(data_directory, "osmFISH_prep_cell_coordinates.txt")
meta_path = paste0(data_directory, "osmFISH_prep_cell_metadata.txt")
# Create instructions
# Optional: Specify a path to a Python executable within a conda or miniconda
# environment. If set to NULL (default), the Python executable within the previously
# installed Giotto environment will be used.
my_python_path = NULL # alternatively, "/local/python/path/python" if desired.
instrs = createGiottoInstructions(python_path = my_python_path,
save_dir = results_directory,
plot_format = 'png',
dpi = 200,
height = 9,
width = 9)
# Create Giotto object
custom_gobject = createGiottoObject(expression = osm_exprs,
spatial_locs = osm_locs,
instructions = instrs)
# Add field annotations as cell metadata
metadata = data.table::fread(file = meta_path)
custom_gobject = addCellMetadata(custom_gobject, new_metadata = metadata,
by_column = T, column_cell_ID = 'CellID')
# Check which Giotto instructions are associated with the Giotto object
showGiottoInstructions(custom_gobject)
Note that although parameters show_plot, return_plot, and save_plot were not specified within the call to createGiottoInstructions, default values were provided to these instruction parameters. All instruction parameters have default values, such that createGiottoInstructions may be called with some or no arguments yet all instruction parameters will have a value after its execution.
Alternatively, a named list may also be provided to the instructions argument of createGiottoObject. However, ensure that all arguments to createGiottoInstructions are defined when providing instructions as a named list, since default values are only applied to instructions when made with createGiottoInstructions. Note that python_path must be specified when providing instructions as a named list, and may not be provided as NULL.
The giottoInstructions may be changed, or completely replaced:
# Change a previously set parameter, e.g. change dpi = 200 to dpi = 300
custom_gobject = changeGiottoInstructions(custom_gobject,
param = 'dpi',
new_value = 300)
# Observe that the instructions have changed
showGiottoInstructions(custom_gobject)
# Create new instructions using a named list
sub_results_directory = paste0(results_directory, 'specific_results/')
my_python_path = custom_gobject@instructions$python_path
new_instrs = list(python_path = my_python_path,
show_plot = TRUE,
return_plot = FALSE,
save_plot = TRUE,
save_dir = sub_results_directory,
plot_format = 'jpg',
dpi = 250,
units = 'in',
height = 12,
width = 12,
is_docker = FALSE)
# Change all instructions
custom_gobject = replaceGiottoInstructions(custom_gobject,
instructions = new_instrs)
# Observe that the instructions have changed
showGiottoInstructions(custom_gobject)
Plotting Data from a Giotto Object
Each plotting function in Giotto has three important binary parameters:
- show_plot: print the plot to the console, default is TRUE
- return_plot: return the plot as an object, default is TRUE
- save_plot: automatically save the plot, default is FALSE
These parameters are stored within a giottoObject that was provided instructions from createGiottoInstructions and are provided to plotting functions accordingly. To change these parameters from the default values, the instructions may be changed or replaced, or these parameters may be manually overwritten within plotting functions.
See showSaveParameters and the Saving Options tutorial for alternative methods to save plots.
# Plot according to Giotto Instructions (default)
spatPlot(custom_gobject)
# Plot clusters, create, and save to a new subdirectory, all while overwriting formatting
spatPlot(custom_gobject,
cell_color = 'ClusterName',
save_plot = TRUE,
return_plot = TRUE,
show_plot = TRUE,
save_param = list(save_folder = 'plots/', # Create subdirectory
save_name = 'cell_clusters',
save_format = 'png',
units = 'in',
base_height = 9,
base_width = 9))
Giotto Object Structure
Usage of the Giotto package revolves around the giottoObject. This is an S4 object class that holds spatial expression data and facilitates its manipulation and visualization with the Giotto package's functions. Additional metadata and other outputs generated from certain Giotto functions, which may used in downstream analyses, are also be stored within the giottoObject. Its self-contained nature renders a convenient representation of the entire spatial experiment and is why most Giotto functions take a given giottoObject as input and return a giottoObject as output.
Data is organized within the giottoObject in defined slots as described in the diagram below.
{width=100%}
{width=100%}
{width=100%}
Nested Organization of the Giotto Object
Spatial data has tiered organization and thus so too does Giotto. Tissue regions can be divided down into individual cells and then further into organelles.
The data structure of Giotto mirrors biological structure by nesting data within slots related to spatial data by spat_unit (spatial unit).
Additonally, to facilitate the integration of multiple -omic data, slots related to feature data will be nested with feat_type (feature type).
Included below is a description of this nesting and also, for advanced users and contributors/developers, accessor functions for the slots are also provided.
# Code snippet from https://bookdown.org/yihui/rmarkdown-cookbook/font-color.html
colorize <- function(x, color) {
if (knitr::is_latex_output()) {
sprintf("\\textcolor{%s}{%s}", color, x)
} else if (knitr::is_html_output()) {
sprintf("<span style='color: %s;'>%s</span>", color,
x)
} else x
}
Slots and Subnesting
Slot Nested Example Accessors
------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------
@expression r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("raw", "#801C34") get_expression_values()
set_expression_values()
showGiottoExpression()
@cell_metadata r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB") pDataDT()
addCellMetadata()
@feat_metadata r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB") fDataDT()
addFeatMetadata()
@spatial_grid r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("grid", "#4C94A0")-r colorize("grid", "#801C34") get_spatialGrid()
set_spatialGrid()
showGiottoSpatGrids()
@nn_network r colorize("spat_unit", "#4C94A0")-r colorize("method", "#F6851F")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("sNN", "#F6851F")-r colorize("sNN_results1", "#801C34") get_NearestNetwork()
set_NearestNetwork()
@dimension_reduction r colorize("approach", "#6C04D0")-r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("method", "#F6851F")-r colorize("name", "#801C34") r colorize("cells", "#6C04D0")-r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("pca", "#F6851F")-r colorize("pca", "#801C34") get_dimReduction()
set_dimReduction()
showGiottoDimRed()
@spatial_enrichment r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("results1", "#801C34") get_spatial_enrichment()
set_spatial_enrichment()
showGiottoSpatEnrichments()
@spatial_info r colorize("spat_unit", "#4C94A0") r colorize("cell", "#4C94A0") get_polygon_info()
set_polygon_info()
showGiottoSpatialInfo()
@spatial_locs r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("raw", "#801C34") get_spatial_locations()
set_spatial_locations()
showGiottoSpatLocs()
@spatial_network r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("Delaunay_network1", "#801C34") get_spatialNetwork()
set_spatialNetwork()
showGiottoSpatNetworks()
@feat_info r colorize("feat_type", "#E88EBB") r colorize("rna", "#E88EBB") get_feature_info()
set_feature_info()
showGiottoFeatInfo()
@images r colorize("name", "#801C34") r colorize("image", "#801C34") getGiottoImage()
addGiottoImage()
showGiottoImageNames()
@largeImages r colorize("name", "#801C34") r colorize("image", "#801C34") getGiottoImage()
addGiottoImage()
showGiottoImageNames()
@instructions replaceGiottoInstructions()
showGiottoInstructions()
@OS_platform
@join_info
@parameters
------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------
drieslab/Giotto_site_suite documentation built on April 26, 2023, 11:51 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
How to create a Giotto Object
In this tutorial, the methodology and syntax to create a giottoObject is shown and osmFISH data is used throughout the tutorial.
Import Giotto and Download the Data
To download this data, please ensure that wget is installed locally.
library(Giotto) genv_exists = checkGiottoEnvironment() if(!genv_exists){ # The following command need only be run once to install the Giotto environment. installGiottoEnvironment() } # Specify path from which data may be retrieved/stored data_directory = paste0(getwd(),'/gobject_data/') # alternatively, "/path/to/where/the/data/lives/" # Specify path to which results may be saved results_directory = paste0(getwd(),'/gobject_results/') # alternatively, "/path/to/store/the/results/" # Download osmFISH dataset to data_directory getSpatialDataset(dataset = 'osmfish_SS_cortex', directory = data_directory, method = 'wget')
Minimum requirements for a Giotto Object
- Expression matrix
- Spatial locations (unnecessary for scRNAseq analysis)
Here, creating a giottoObject with the minimum requirements is shown in two examples. Data formatting guidelines are shown below this code block.
# Example 1. # Create a Giotto object using data directly from file paths osm_exprs = paste0(data_directory, "osmFISH_prep_expression.txt") osm_locs = paste0(data_directory, "osmFISH_prep_cell_coordinates.txt") minimum_gobject1 = createGiottoObject(expression = osm_exprs, spatial_locs = osm_locs) # Example 2. # Create a Giotto object using objects already loaded into workspace expression_matrix = readExprMatrix(path = osm_exprs) # fast method to read expression matrix cell_locations = data.table::fread(file = osm_locs) minimum_gobject2 = createGiottoObject(expression = expression_matrix, spatial_locs = cell_locations)
Expression file formatting
Expression count matrices accepted by Giotto should have columns correlating to individual cells (cell IDs) and rows to individual features (feature IDs).
data.table objects and text-based files ie: (.csv, .tsv, .txt) to be read in should have column 1 be feature IDs.
| |feat_ID| Cell_1| Cell_2| Cell_3| ...|
|-|:------|-------|-------|-------|----|
|1|Gene1 | | | | |
|2|Gene2 | | | | |
|3|Gene3 | | | | |
| |... | | | | |
matrix and DelayedMatrix objects should have feature IDs already incorporated as the rownames.
| | Cell_1| Cell_2| Cell_3| ...|
|:------|-------|-------|-------|----|
|Gene1 | | | | |
|Gene2 | | | | |
|Gene3 | | | | |
|... | | | | |
Locations file formatting
Numerical columns will be interpreted in order of x, y, and (optionally) z coordinate. The first non-numerical column will be taken as cell IDs
| | sdimx| sdimy| sdimz| cell_ID| |-|------|------|------|--------| |1| | | | | |2| | | | | |3| | | | | | | | | | |
*Note: multiple expression files during can be given at once during Giotto object creation by using a named list.
# Arbitrary modifications scaled_matrix = expression_matrix * 1.2 custom_matrix = expression_matrix * 0.5 # Provide multiple expression matrices at once to the Giotto Object # If these matrices are stored in files rather than in the workspace, # file paths may be provided instead of variables multi_expr_gobject = createGiottoObject(expression = list(raw = expression_matrix, scaled = scaled_matrix, custom = custom_matrix), spatial_locs = cell_locations)
Customizing the Giotto Object
By providing values to other createGiottoObject parameters, it is possible to add:
-
Cell or feature (gene) metadata: see addCellMetadata and addFeatMetadata
-
Spatial networks or grids: see Visualizations
-
Dimension reduction: see Clustering
-
Images: see Imaging
-
giottoInstructions: see createGiottoInstructions and below
Providing giottoInstructions allows the specification of:
- An alternative python path if using the Giotto Environment (default) is not desired
- A directory to which resulting plots will save
- Plot formatting
There are four functions to utilize when setting instructions:
- createGiottoInstructions: creates instructions that are provided to createGiottoObject
- showGiottoInstructions: displays the instructions of a Giotto object
- changeGiottoInstructions: replaces 1 or more of the instruction parameters (e.g. plotting format)
- replaceGiottoInstructions: replaces all instructions with new instructions (e.g after subsetting)
Here is an example of a more customized Giotto object.
# Specify data with file paths osm_exprs = paste0(data_directory, "osmFISH_prep_expression.txt") osm_locs = paste0(data_directory, "osmFISH_prep_cell_coordinates.txt") meta_path = paste0(data_directory, "osmFISH_prep_cell_metadata.txt") # Create instructions # Optional: Specify a path to a Python executable within a conda or miniconda # environment. If set to NULL (default), the Python executable within the previously # installed Giotto environment will be used. my_python_path = NULL # alternatively, "/local/python/path/python" if desired. instrs = createGiottoInstructions(python_path = my_python_path, save_dir = results_directory, plot_format = 'png', dpi = 200, height = 9, width = 9) # Create Giotto object custom_gobject = createGiottoObject(expression = osm_exprs, spatial_locs = osm_locs, instructions = instrs) # Add field annotations as cell metadata metadata = data.table::fread(file = meta_path) custom_gobject = addCellMetadata(custom_gobject, new_metadata = metadata, by_column = T, column_cell_ID = 'CellID') # Check which Giotto instructions are associated with the Giotto object showGiottoInstructions(custom_gobject)
Note that although parameters show_plot, return_plot, and save_plot were not specified within the call to createGiottoInstructions, default values were provided to these instruction parameters. All instruction parameters have default values, such that createGiottoInstructions may be called with some or no arguments yet all instruction parameters will have a value after its execution.
Alternatively, a named list may also be provided to the instructions argument of createGiottoObject. However, ensure that all arguments to createGiottoInstructions are defined when providing instructions as a named list, since default values are only applied to instructions when made with createGiottoInstructions. Note that python_path must be specified when providing instructions as a named list, and may not be provided as NULL.
The giottoInstructions may be changed, or completely replaced:
# Change a previously set parameter, e.g. change dpi = 200 to dpi = 300 custom_gobject = changeGiottoInstructions(custom_gobject, param = 'dpi', new_value = 300) # Observe that the instructions have changed showGiottoInstructions(custom_gobject) # Create new instructions using a named list sub_results_directory = paste0(results_directory, 'specific_results/') my_python_path = custom_gobject@instructions$python_path new_instrs = list(python_path = my_python_path, show_plot = TRUE, return_plot = FALSE, save_plot = TRUE, save_dir = sub_results_directory, plot_format = 'jpg', dpi = 250, units = 'in', height = 12, width = 12, is_docker = FALSE) # Change all instructions custom_gobject = replaceGiottoInstructions(custom_gobject, instructions = new_instrs) # Observe that the instructions have changed showGiottoInstructions(custom_gobject)
Plotting Data from a Giotto Object
Each plotting function in Giotto has three important binary parameters:
- show_plot: print the plot to the console, default is TRUE
- return_plot: return the plot as an object, default is TRUE
- save_plot: automatically save the plot, default is FALSE
These parameters are stored within a giottoObject that was provided instructions from createGiottoInstructions and are provided to plotting functions accordingly. To change these parameters from the default values, the instructions may be changed or replaced, or these parameters may be manually overwritten within plotting functions.
See showSaveParameters and the Saving Options tutorial for alternative methods to save plots.
# Plot according to Giotto Instructions (default) spatPlot(custom_gobject) # Plot clusters, create, and save to a new subdirectory, all while overwriting formatting spatPlot(custom_gobject, cell_color = 'ClusterName', save_plot = TRUE, return_plot = TRUE, show_plot = TRUE, save_param = list(save_folder = 'plots/', # Create subdirectory save_name = 'cell_clusters', save_format = 'png', units = 'in', base_height = 9, base_width = 9))
Giotto Object Structure
Usage of the Giotto package revolves around the giottoObject. This is an S4 object class that holds spatial expression data and facilitates its manipulation and visualization with the Giotto package's functions. Additional metadata and other outputs generated from certain Giotto functions, which may used in downstream analyses, are also be stored within the giottoObject. Its self-contained nature renders a convenient representation of the entire spatial experiment and is why most Giotto functions take a given giottoObject as input and return a giottoObject as output.
Data is organized within the giottoObject in defined slots as described in the diagram below.
{width=100%}
{width=100%}
{width=100%}
Nested Organization of the Giotto Object
Spatial data has tiered organization and thus so too does Giotto. Tissue regions can be divided down into individual cells and then further into organelles.
The data structure of Giotto mirrors biological structure by nesting data within slots related to spatial data by spat_unit (spatial unit).
Additonally, to facilitate the integration of multiple -omic data, slots related to feature data will be nested with feat_type (feature type).
Included below is a description of this nesting and also, for advanced users and contributors/developers, accessor functions for the slots are also provided.
# Code snippet from https://bookdown.org/yihui/rmarkdown-cookbook/font-color.html colorize <- function(x, color) { if (knitr::is_latex_output()) { sprintf("\\textcolor{%s}{%s}", color, x) } else if (knitr::is_html_output()) { sprintf("<span style='color: %s;'>%s</span>", color, x) } else x }
Slots and Subnesting
Slot Nested Example Accessors
------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------
@expression r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("raw", "#801C34") get_expression_values()
set_expression_values()
showGiottoExpression()
@cell_metadata r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB") pDataDT()
addCellMetadata()
@feat_metadata r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB") fDataDT()
addFeatMetadata()
@spatial_grid r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("grid", "#4C94A0")-r colorize("grid", "#801C34") get_spatialGrid()
set_spatialGrid()
showGiottoSpatGrids()
@nn_network r colorize("spat_unit", "#4C94A0")-r colorize("method", "#F6851F")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("sNN", "#F6851F")-r colorize("sNN_results1", "#801C34") get_NearestNetwork()
set_NearestNetwork()
@dimension_reduction r colorize("approach", "#6C04D0")-r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("method", "#F6851F")-r colorize("name", "#801C34") r colorize("cells", "#6C04D0")-r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("pca", "#F6851F")-r colorize("pca", "#801C34") get_dimReduction()
set_dimReduction()
showGiottoDimRed()
@spatial_enrichment r colorize("spat_unit", "#4C94A0")-r colorize("feat_type", "#E88EBB")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("rna", "#E88EBB")-r colorize("results1", "#801C34") get_spatial_enrichment()
set_spatial_enrichment()
showGiottoSpatEnrichments()
@spatial_info r colorize("spat_unit", "#4C94A0") r colorize("cell", "#4C94A0") get_polygon_info()
set_polygon_info()
showGiottoSpatialInfo()
@spatial_locs r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("raw", "#801C34") get_spatial_locations()
set_spatial_locations()
showGiottoSpatLocs()
@spatial_network r colorize("spat_unit", "#4C94A0")-r colorize("name", "#801C34") r colorize("cell", "#4C94A0")-r colorize("Delaunay_network1", "#801C34") get_spatialNetwork()
set_spatialNetwork()
showGiottoSpatNetworks()
@feat_info r colorize("feat_type", "#E88EBB") r colorize("rna", "#E88EBB") get_feature_info()
set_feature_info()
showGiottoFeatInfo()
@images r colorize("name", "#801C34") r colorize("image", "#801C34") getGiottoImage()
addGiottoImage()
showGiottoImageNames()
@largeImages r colorize("name", "#801C34") r colorize("image", "#801C34") getGiottoImage()
addGiottoImage()
showGiottoImageNames()
@instructions replaceGiottoInstructions()
showGiottoInstructions()
@OS_platform
@join_info
@parameters
------------------------ ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -----------------------------------------------------------------------------------
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.