Test data for SpaCCI

Description

This dataset includes:

• DB_dataframe: A data frame of self-identified LR, including columns of "ligand","receptor", and "interaction_name

Usage

data(DB_dataframe)

Format

An object of class data.frame with 1 rows and 3 columns.

Examples

library(SpaCCI)
data(DB_dataframe)

Find the spatial neighborhood Spot IDs

Description

This function identifies the spatial neighborhood Spot IDs around a given center Spot ID within a specified radius.

Usage

Find_regional_IDs(
  object,
  spatial_coord,
  centerID,
  enhanced = FALSE,
  radius,
  avern = 5
)

Arguments

Value

A list containing:

centerID

The input center Spot_ID.

closeID

The Spot_IDs of the neighboring spots within the specified radius.

unit

The unit distance used to determine the neighborhood.

Perform a Deranged Shuffle of Cell Types

Description

This function takes a vector of cell types and returns a shuffled version where no element remains in its original position.

Usage

GetShuffledCT(CellType)

Arguments

Value

A character vector of the same length as 'CellType', with elements shuffled such that no element remains in its original position.

Examples

original <- c("B_cell", "T_cell", "NK_cell", "Macrophage")
shuffled <- GetShuffledCT(original)
print(shuffled)

Perform Global Permutations

Description

This function performs global permutations on the spatial transcriptomics data.

Usage

Global_Permutations(
  permutationMatrix,
  permut_col,
  cellPropMatrix,
  spotGeneMatrix,
  LigandVectorIndex,
  ReceptorVectorIndex,
  null_expression,
  nBoot
)

Arguments

Value

A matrix with the results of the global permutations.

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using a selected database. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix, filtering based on a specified expression percentage threshold. The function supports multiple databases including CellChat, CellPhoneDB, Cellinker, ICELLNET, and ConnectomeDB.

Usage

LR_database(
  species,
  database_name,
  gene_spot_expression_dataframe,
  percentage = NULL
)

Arguments

Value

A list containing:

possible_LR_pairs

A data frame of L-R pairs where all genes are present in the gene_spot_expression_dataframe and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_LR_pairs_info

A data frame with detailed information about the identified L-R pairs, including their original annotations from the selected database.

Examples

library(SpaCCI)
#Load the example data
data(test_data)
gene_spot_df <- test_data$gene_spot_df
result <- LR_database(species = "Human",
            database_name = "CellChat",
            gene_spot_expression_dataframe = gene_spot_df)

Perform Local and Regional Permutations

Description

This function performs local and regional permutations on the spatial transcriptomics data.

Usage

Local_Regional_Permutations(
  permutationMatrix,
  permut_col,
  cellPropMatrix,
  spotGeneMatrix,
  LigandVectorIndex,
  ReceptorVectorIndex,
  null_expression,
  nBoot
)

Arguments

Value

A matrix with the results of the local and regional permutations.

Infer Cell-Cell Interactions on a Global Scale

Description

This function infers cell-cell interactions on a global scale using spatial transcriptomics data. It applies permutation testing to identify significant ligand-receptor interactions across all spots.

Usage

SpaCCI_global(
  gene_spot_df,
  spot_cell_prop_df,
  spatial_coord,
  matching_L_R_pairs,
  matching_L_R_pairs_info
)

Arguments

Value

A list containing:

pvalue_df

A data frame of inferred interactions across the global scale, including information on ligand and receptor cell types, interaction strength, P-values, and adjusted P-values.

Infer Cell-Cell Interactions on a Local Scale

Description

This function infers cell-cell interactions on a local scale using spatial transcriptomics data. It utilizes permutation testing to identify significant ligand-receptor interactions within specified neighborhoods around randomly selected center spots.

Usage

SpaCCI_local(
  gene_spot_df,
  spot_cell_prop_df,
  spatial_coord,
  prop,
  radius,
  matching_L_R_pairs,
  matching_L_R_pairs_info
)

Arguments

Value

A list containing:

dataframelist

A list of data frames, each representing the inferred interactions for a specific center spot. Each data frame includes information on ligand and receptor cell types, P-values, and adjusted P-values.

RegionIDs_matrix

A list of matrices, each containing the IDs of the spots within the specified radius of each center spot.

Infer Cell-Cell Interactions in a Specified Region

Description

This function infers cell-cell interactions within a specified region using spatial transcriptomics data. It applies permutation testing to identify significant ligand-receptor interactions in the region.

Usage

SpaCCI_region(
  gene_spot_df,
  spot_cell_prop_df,
  spatial_coord,
  region_spot_IDs,
  matching_L_R_pairs,
  matching_L_R_pairs_info
)

Arguments

Value

A list containing:

pvalue_df

A data frame of inferred interactions within the specified region, including information on ligand and receptor cell types, P-values, and adjusted P-values.

Plot SpaCCI Results on Chord Diagram

Description

This function generates a chord diagram to visualize cell-cell interactions based on ligand-receptor pairs. The interactions can be filtered by specific cell types, pathways, or interaction names.

Usage

plot_SpaCCI_chordDiagram(
  SpaCCI_Result_List,
  specific_celltypes = NULL,
  pathway_name = NULL,
  L_R_pair_name = NULL,
  color = NULL,
  alpha = 0.05
)

Arguments

Value

A chord diagram plot visualizing the significant cell-cell interactions.

Examples

library(SpaCCI)
library(dplyr)
library(circlize)
data(result_global)
celltypes <- c("beta" , "delta" , "ductal","macrophage",
                "activated_stellate", "quiescent_stellate")
# Run the result chordDiagram for global analysis
plot_SpaCCI_chordDiagram(SpaCCI_Result_List = result_global,
                         specific_celltypes = c(celltypes),
                         L_R_pair_name  = "AREG_EGFR")

Plot SpaCCI Results on the heatmap Visualize inferred significant cell-cell interactions using a heatmap

Description

Plot SpaCCI Results on the heatmap Visualize inferred significant cell-cell interactions using a heatmap

Usage

plot_SpaCCI_heatmap(
  SpaCCI_Result_List,
  specific_celltypes = NULL,
  pathways = NULL,
  interaction = NULL,
  log1p_transform = FALSE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  scale = "none",
  cluster_cols = TRUE,
  cluster_rows = TRUE,
  border_color = "white",
  fontsize_row = 11,
  fontsize_col = 11,
  family = "Arial",
  main = "",
  treeheight_col = 0,
  treeheight_row = 0,
  low_col = "dodgerblue4",
  mid_col = "peachpuff",
  high_col = "deeppink4",
  alpha = 0.05,
  return_tables = FALSE,
  symmetrical = FALSE,
  ...
)

Arguments

Value

If 'return_tables' is FALSE (default), the function returns a heatmap object created by pheatmap, showing the count of significant cell-cell interactions. If 'return_tables' is TRUE, the function returns a list containing:

heatmap

The heatmap object showing the significant cell-cell interactions.

heatmap_countmatrix

The matrix used to generate the heatmap, with cell types as rows and columns, and counts of significant interactions as values.

table

A data frame summarizing the counts of significant interactions between each ligand and receptor cell type combination.

Examples

library(SpaCCI)
library(dplyr)
library(reshape2)
library(grDevices)
library(pheatmap)
data(result_global)
celltypes <- c("beta" , "delta" , "ductal","macrophage",
                "activated_stellate", "quiescent_stellate")
plot_SpaCCI_heatmap(SpaCCI_Result_List = result_global,
                    symmetrical = FALSE, cluster_cols = FALSE, return_tables = FALSE,
                    cluster_rows = FALSE, #cellheight = 10, cellwidth = 10,
                    specific_celltypes = c(celltypes),
                    main= "Cell-Cell Interaction Count")

Plot SpaCCI Localized Interaction Results

Description

This function provides a unified interface to visualize the localized cell-cell interaction patterns inferred by SpaCCI, either using a Seurat object with a spatial image or a spatial coordinates data frame.

Usage

plot_SpaCCI_local(
  Seurat_Object = NULL,
  spatial_coordinates_dataframe = NULL,
  SpaCCI_local_Result_List,
  Ligand_cell_type,
  Receptor_cell_type,
  spot_plot_size,
  specific_LR_pair_name = NULL,
  significant_cutoff = 0.05
)

Arguments

Value

A plot object showing the localized interaction patterns. The plot will be generated using either the Seurat object or the spatial coordinates data frame, depending on the input provided.

Examples

# Plot localized SpaCCI results using Seurat object
library(SpaCCI)
library(dplyr)
data(result_local)
data(result_local_spatial_coords_df)
spatial_coords_df <- result_local_spatial_coords_df
#plot_SpaCCI_local(Seurat_Object = seurat_object,.....)

# Plot localized SpaCCI results using spatial coordinates
plot_SpaCCI_local(spatial_coordinates_dataframe = spatial_coords_df,
                  SpaCCI_local_Result_List = result_local,
                  Ligand_cell_type = "ductal",
                  Receptor_cell_type = "activated_stellate",
                  spot_plot_size = 3)

Plot SpaCCI Localized Interaction Strength Results

Description

This function provides a unified interface to visualize the localized cell-cell interaction strength patterns inferred by SpaCCI, either using a Seurat object with a spatial image or a spatial coordinates data frame.

Usage

plot_SpaCCI_local_Strength(
  Seurat_Object = NULL,
  spatial_coordinates_dataframe = NULL,
  SpaCCI_local_Result_List,
  Ligand_cell_type,
  Receptor_cell_type,
  spot_plot_size,
  specific_LR_pair_name = NULL
)

Arguments

Value

A plot object showing the localized interaction strength patterns. The plot will be generated using either the Seurat object or the spatial coordinates data frame, depending on the input provided.

Examples

# Plot localized SpaCCI results using Seurat object
library(SpaCCI)
library(dplyr)
data(result_local)
data(result_local_spatial_coords_df)
spatial_coords_df <- result_local_spatial_coords_df
#plot_SpaCCI_local(Seurat_Object = seurat_object,.....)

# Plot localized SpaCCI results using spatial coordinates
plot_SpaCCI_local_Strength(spatial_coordinates_dataframe = spatial_coords_df,
                           SpaCCI_local_Result_List = result_local,
                           Ligand_cell_type = "ductal",
                           Receptor_cell_type = "activated_stellate",
                           spot_plot_size = 3)

Plot Localized Hotspot Strength Pattern on Seurat Object

Description

Visualize the inferred cell-cell interaction localized pattern on the tissue image with Seurat_object

Usage

plot_Strength_Seurat(
  Seurat_object,
  resultdf_list,
  RegionIDs_matrix,
  celltype_ligand,
  celltype_receptor,
  plot_size,
  L_R_pair_name = NULL
)

Arguments

Value

The localized plot from the inferred cell-cell interaction on the local scale.

Examples

# Not Run

# Run localized hotspot plot
Result <- run_SpaCCI(..., analysis_scale = "local",...)
local_plot <- plot_Strength_Seurat(Seurat_object = gene_spot_df,
                                    resultdf_list = Result$dataframelist,
                                    RegionIDs_matrix = Result$RegionIDs_matrix,
                                    celltype_ligand = "Beta_cells",
                                    celltype_receptor = "T_ells",
                                    plot_size = 3)

Plot Localized Hotspot Strength Pattern

Description

Visualize the inferred cell-cell interaction localized pattern if NOT using Seurat Object

Usage

plot_Strength_localized(
  spatial_coord,
  resultdf_list,
  RegionIDs_matrix,
  celltype_ligand,
  celltype_receptor,
  plot_size,
  L_R_pair_name = NULL
)

Arguments

Value

The localized plot from the inferred cell-cell interaction on the local scale.

Examples

# Run localized hotspot plot
Result <- run_SpaCCI(..., analysis_scale = "local",...)
local_plot <- plot_Strength_localized(spatial_coord = spatial_coords_df,
                                      resultdf_list = Result$dataframelist,
                                      RegionIDs_matrix = Result$RegionIDs_matrix,
                                      celltype_ligand = "Beta_cells",
                                      celltype_receptor = "T_ells",
                                      plot_size = 3)

Plot Localized Hotspot Pattern

Description

Visualize the inferred cell-cell interaction localized pattern if NOT using Seurat Object

Usage

plot_localized(
  spatial_coord,
  resultdf_list,
  RegionIDs_matrix,
  celltype_ligand,
  celltype_receptor,
  plot_size,
  L_R_pair_name = NULL,
  alpha = 0.05
)

Arguments

Value

The localized plot from the inferred cell-cell interaction on the local scale.

Examples

# Run localized hotspot plot
Result <- run_SpaCCI(..., analysis_scale = "local",...)
local_plot <- plot_localized(spatial_coord = spatial_coords_df,
                             resultdf_list = Result$dataframelist,
                             RegionIDs_matrix = Result$RegionIDs_matrix,
                             celltype_ligand = "Beta_cells",
                             celltype_receptor = "T_ells",
                             plot_size = 3)

Plot Localized Hotspot Pattern on Seurat Object

Description

Visualize the inferred cell-cell interaction localized pattern on the tissue image with Seurat_object

Usage

plot_localized_Seurat(
  Seurat_object,
  resultdf_list,
  RegionIDs_matrix,
  celltype_ligand,
  celltype_receptor,
  plot_size,
  L_R_pair_name = NULL,
  alpha = 0.05
)

Arguments

Value

The localized plot from the inferred cell-cell interaction on the local scale.

Examples

# Not Run

# Run localized hotspot plot
Result <- run_SpaCCI(..., analysis_scale = "local",...)
local_plot <- plot_localized_Seurat(Seurat_object = gene_spot_df,
                                    resultdf_list = Result$dataframelist,
                                    RegionIDs_matrix = Result$RegionIDs_matrix,
                                    celltype_ligand = "Beta_cells",
                                    celltype_receptor = "T_ells",
                                    plot_size = 3)

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

Cellinker Database: This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using data from the Cellinker database. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix, filtering based on a specified expression percentage threshold.

Usage

possible_L_R_pairs_Cellinker(
  species,
  gene_spot_expression_dataframe,
  percentage
)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe' and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the identified L-R pairs, including their original annotations from the Cellinker dataset.

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

ICELLENT Database: This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using data from the ICELLNET database. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix, filtering based on a specified expression percentage threshold.

Usage

possible_L_R_pairs_ICELLNET(gene_spot_expression_dataframe, percentage)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe' and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the identified L-R pairs, including their original annotations from the ICELLNET dataset.

CellChat Database: Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using a subset of the CellChat database. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix.

Usage

possible_L_R_pairs_cellchat(
  species,
  gene_spot_expression_dataframe,
  percentage
)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe'. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the L-R pairs, including the original annotations from the CellChatDB.

Examples

library(SpaCCI)
#Load the example data
load(system.file("extdata", "Tutorial_example_data.rda", package = "SpaCCI"))
Example_Seurat <- NormalizeData(Example_Seurat)
gene_spot_df <- as.data.frame(Example_Seurat@assays$Spatial@data)
result <- possible_L_R_pairs_cellchat(CellChatDB.human,
      gene_spot_expression_dataframe = gene_spot_df)

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

CellPhone Database: This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using data from a CellPhoneDB dataset. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix and filters based on a specified expression percentage threshold.

Usage

possible_L_R_pairs_cellphoneDB(gene_spot_expression_dataframe, percentage)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe' and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the identified L-R pairs, including their original annotations from the CellPhoneDB dataset.

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

ConnectomeDB 2020 Database: This function identifies possible ligand-receptor (L-R) pairs based on gene expression data.

Usage

possible_L_R_pairs_connectome(gene_spot_expression_dataframe, percentage)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe' and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the identified L-R pairs, including their original annotations from the ConnectomeDB 2020 dataset.

Select Closest Spatial IDs to a Center Point: this is used for permutation

Description

This function identifies and returns the IDs of the closest spatial points to a specified center point based on Euclidean distance.

Usage

random_region(spatial_coord, center_id, n_ids)

Arguments

Value

A character vector of the 'n_ids' closest IDs to the specified center ID.

Examples

spatial_coord <- data.frame(
  imagecol = c(1, 2, 3, 4, 5),
  imagerow = c(5, 4, 3, 2, 1),
  row.names = c("Spot1", "Spot2", "Spot3", "Spot4", "Spot5")
)
center_id <- "Spot3"
closest_ids <- random_region(spatial_coord, center_id, 3)
print(closest_ids)

result_global data for SpaCCI

Description

This example dataset is the result of running the run_SpaCCI function. It contains the inferred cell-cell interactions across the global scale.

Usage

data(result_global)

Format

A list containing:

pvalue_df

A data frame of p-values and adjusted p-values for cell-cell interactions.

Details

These objects can be used for testing and running example analyses with the SpaCCI package.

Examples

  data(result_global)
  print(result_global)

result_local data and spatial coordinates for SpaCCI Local Regional Data

Description

This dataset contains the results of spatial neighborhood analyses. These objects can be used for testing and running example analyses with the SpaCCI package.

Usage

data(result_local)

Format

A list containing:

dataframelist

A list of data frames, each containing p-value results for spatial neighborhoods.

RegionIDs_matrix

A list of matrices, each containing the spot IDs for specific neighborhoods.

Examples

  data(result_local)
  print(result_local)

Local Spatial Coordinates Data Frame

Description

A dataset containing spatial coordinates for local analysis, typically used in spatial transcriptomics.

Usage

data(result_local)

Format

A data frame with X rows and 3 columns:

imagecol

The x-coordinate of the spot in the image.

imagerow

The y-coordinate of the spot in the image.

Spot_ID

The unique identifier for each spatial spot.

Examples

data(result_local_spatial_coords_df)
head(result_local_spatial_coords_df)

result_regional data for SpaCCI

Description

This example dataset is the result of running the run_SpaCCI function. It contains the inferred cell-cell interactions across the regional scale.

Usage

data(result_regional)

Format

A list containing:

pvalue_df

A data frame of p-values and adjusted p-values for cell-cell interactions.

Details

These objects can be used for testing and running example analyses with the SpaCCI package.

Examples

  data(result_regional)
  print(result_regional)

Run SpaCCI Analysis

Description

This function runs the SpaCCI analysis to infer cell-cell interactions based on ligand-receptor pairs at global, regional, or local spatial scales. It integrates gene expression data, cell type proportions, and spatial coordinates with a user-specified ligand-receptor database.

Usage

run_SpaCCI(
  gene_spot_expression_dataframe,
  spot_cell_proportion_dataframe,
  spatial_coordinates_dataframe,
  LR_database_list,
  specific_LR_pair = NULL,
  analysis_scale,
  region_spot_IDs = NULL,
  local_scale_proportion = 1,
  neighborhood_radius = 2.5
)

Arguments

Details

The function supports three scales of analysis:

global

Analyzes interactions across the entire dataset.

regional

Analyzes interactions within a specified region of spots. Requires region_spot_IDs.

local

Analyzes localized hotspot of interactions for specific ligand-receptor pairs on the entire slides. Requires specific_LR_pair.

Value

A list containing:

If analysis_scale is "local":

A list containing:

dataframelist

A list of data frames, each representing the inferred interactions for a specific center spot. Each data frame includes information on ligand and receptor cell types, P-values, and adjusted P-values.

RegionIDs_matrix

A list of matrices, each containing the IDs of the spots within the specified radius of each center spot.

If analysis_scale is "regional" or "global":

A list containing:

pvalue_df

A data frame of inferred interactions within the specified region or globally, including information on ligand and receptor cell types, P-values, and adjusted P-values.

Examples

library(SpaCCI)
library(nnls)
library(FNN)
library(Matrix)
#Load the example data
data(test_data)
gene_spot_df <- test_data$gene_spot_df
cell_prop_df <- test_data$cell_prop_df
spatial_coords_df <- test_data$spatial_coords_df

result <- LR_database(species = "Human",
                      database_name = "CellChat",
                      gene_spot_expression_dataframe = gene_spot_df)
# global
result_global <- run_SpaCCI(gene_spot_expression_dataframe = gene_spot_df,
                            spot_cell_proportion_dataframe = cell_prop_df,
                            spatial_coordinates_dataframe = spatial_coords_df,
                            LR_database_list = result,
                            analysis_scale = "global")

# local
result_local <- run_SpaCCI(gene_spot_expression_dataframe = gene_spot_df,
                          spot_cell_proportion_dataframe = cell_prop_df,
                          spatial_coordinates_dataframe = spatial_coords_df,
                          LR_database_list = result,
                          specific_LR_pair = "EDN1_EDNRA",
                          analysis_scale = "local",
                          local_scale_proportion = 0.1,
                          neighborhood_radius = 2.5)

Generate a Color Palette

Description

This function generates a color palette. It selects colors from a predefined color space, and if more colors are needed than are available in the predefined set, it generates a palette using color interpolation.

Usage

scPalette(n)

Arguments

Value

A character vector of colors in hexadecimal format.

Examples

# Generate a palette with 5 colors
palette <- scPalette(5)
print(palette)

# Generate a palette with 30 colors
large_palette <- scPalette(30)
print(large_palette)

Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication

Description

Self_identified Database: This function identifies possible ligand-receptor (L-R) pairs for cell-cell communication analysis using data from the self identified database. It checks for the presence of all genes involved in each L-R pair within the provided gene expression matrix, filtering based on a specified expression percentage threshold.

Usage

self_identified_LR(
  DB_dataframe,
  gene_spot_expression_dataframe,
  percentage = NULL
)

Arguments

Value

A list containing:

possible_L_R_pairs

A data frame of L-R pairs where all genes are present in the 'gene_spot_expression_dataframe' and meet the expression threshold. The data frame includes the ligand and receptor vectors, and the combined gene vectors.

possible_L_R_pairs_details

A data frame with detailed information about the identified L-R pairs, including their original annotations from the ICELLNET dataset.

Examples

library(SpaCCI)
#Load the example data
data(test_data)
#Load the example self-identified database
#(for more instructions please check the online tutorial)
data(DB_dataframe)
gene_spot_df <- test_data$gene_spot_df
result <- self_identified_LR( DB_dataframe = DB_dataframe,
                              gene_spot_expression_dataframe = gene_spot_df)

Test data for SpaCCI

Description

This dataset includes:

• gene_spt_df: A data frame of spot-level gene expression.

• cell_prop_df: A data frame with cell type proportions.

• spatial_coords_df: A data frame of spatial coordinates.

Usage

data(test_data)

Format

An object of class list of length 3.

Examples

library(SpaCCI)
data(test_data)