Package 'SpaCCI'

Title:	Spatially Aware Cell-Cell Interaction Analysis
Description:	Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' <http://www.cellchat.org/>, 'CellPhoneDB' <https://www.cellphonedb.org/>, 'Cellinker' <https://www.rna-society.org/cellinker/>, 'ICELLNET' <https://github.com/soumelis-lab/ICELLNET>, and 'ConnectomeDB' <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.
Authors:	Li-Ting Ku [aut, cre]
Maintainer:	Li-Ting Ku <[email protected]>
License:	GPL (>= 2)
Version:	1.0.2
Built:	2024-10-25 05:28:05 UTC
Source:	CRAN

Help Index

Find the spatial neighborhood Spot IDs
Perform a Deranged Shuffle of Cell Types
Perform Global Permutations
Perform Local and Regional Permutations
Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Plot Localized Hotspot Pattern
Plot Localized Hotspot Pattern on Seurat Object
Plot SpaCCI Results on Chord Diagram
Plot SpaCCI Results on the heatmap Visualize inferred significant cell-cell interactions using a heatmap
Plot SpaCCI Localized Interaction Results
CellChat Database: Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Identify Possible Ligand-Receptor Pairs for Cell-Cell Communication
Select Closest Spatial IDs to a Center Point: this is used for permutation
result_global data for SpaCCI
result_local data for SpaCCI
Run SpaCCI Analysis
Generate a Color Palette
Infer Cell-Cell Interactions on a Global Scale
Infer Cell-Cell Interactions on a Local Scale
Infer Cell-Cell Interactions in a Specified Region
Test data for SpaCCI

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

Arguments

`object`	An object that could be either 1. A Seurat object, or 2. A data frame where the columns are Spot_IDs (i.e., the gene*spot expression matrix).
`spatial_coord`	A data frame of the spatial coordinates. The column names should include 'c("Spot_ID", "imagerow", "imagecol")', and the row names must be the Spot_ID, which is the same as the row names in the cell type proportion data frame or the column names of the gene*spot expression data frame.
`centerID`	A vector of length 1, representing a single Spot_ID that serves as the center for the neighborhood.
`enhanced`	Logical; if 'TRUE', enhances the Seurat object by marking the neighborhood in a special way. Defaults to 'FALSE'.
`radius`	The radius of the spatial neighborhood, specified as a numeric value.
`avern`	Numeric; the number of samples to average over when determining the unit distance. Defaults to 5.

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)
GetShuffledCT(CellType)

Arguments

CellType

A character vector representing the cell types to be shuffled.

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)


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

Arguments

`permutationMatrix`	A matrix containing permutations.
`permut_null_regionMatrix`	A matrix of null region permutations.
`permut_col`	A column matrix of permutations.
`cellPropMatrix`	A matrix of cell type proportions.
`spotGeneMatrix`	A matrix of gene expressions at spots.
`LigandVectorIndex`	A vector of ligand indices.
`ReceptorVectorIndex`	A vector of receptor indices.
`null_expression`	A matrix of null expression values.
`nBoot`	Number of bootstrap iterations.

Value

A matrix with the results of the global permutations.

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

Arguments

`permutationMatrix`	A matrix containing permutations.
`permut_col`	A column matrix of permutations.
`cellPropMatrix`	A matrix of cell type proportions.
`spotGeneMatrix`	A matrix of gene expressions at spots.
`LigandVectorIndex`	A vector of ligand indices.
`ReceptorVectorIndex`	A vector of receptor indices.
`null_expression`	A matrix of null expression values.
`nBoot`	Number of bootstrap iterations.

Value

A matrix with the results of the local and regional 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 = 10
)
LR_database(
  species,
  database_name,
  gene_spot_expression_dataframe,
  percentage = 10
)

Arguments

`species`	A string specifying the species (`"Human"` or `"Mouse"`).
`database_name`	A string specifying the L-R database to use. Options include `"CellChat"`, `"CellPhoneDB"`, `"Cellinker"`, `"ICELLNET"`, and `"ConnectomeDB"`.
`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is `10`, meaning the gene express over 10% of spots.

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

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

Arguments

`spatial_coord`	A data frame of the spatial coordinates. The columns should include `"Spot_ID"`, `"imagerow"`, and `"imagecol"`. And the row names must be the names of `"Spot_ID"`, which is the same as the rownames in cell type proportion data frame or the colnames of the gene* spot expression data frame
`resultdf_list`	A result of data frame list from the output of `run_SpaCCI(..., analysis_scale = "local",...)` `dataframelist`
`RegionIDs_matrix`	A result of matrix list from the output of `run_SpaCCI(..., analysis_scale = "local",...)` `RegionIDs_matrix`
`celltype_ligand`	Ligand cell type string inputted by user, the name of the cell type should match the names in the `spot_cell_proportion_dataframe` during the `run_SpaCCI` analysis.
`celltype_receptor`	Receptor cell type string inputted by user, the name of the cell type should match the names in the `spot_cell_proportion_dataframe` during the `run_SpaCCI` analysis.
`plot_size`	As this function incorporate with `Seurat`'s `SpatialFeaturePlot`, this parameter could control the plotting size of the each spot.
`L_R_pair_name`	Initially this is set to `NULL`, if one is interested in a specific Ligand-Receptor pair, then one could specify the L_R_pair_name here. Note: the input name should match the L-R pair name exists in the dataframe in the output of SpaCCI_local "dataframelist".
`alpha`	This is the significant cutoff for the adjusted-p-value of thr permutation test. Initially this is set to `0.05`, one could adjust the cutoff.

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)


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

Arguments

`Seurat_object`	A Seurat object
`resultdf_list`	A result of data frame list from the output of `run_SpaCCI(..., analysis_scale = "local",...)` `dataframelist`
`RegionIDs_matrix`	A result of matrix list from the output of `run_SpaCCI(..., analysis_scale = "local",...)` `RegionIDs_matrix`
`celltype_ligand`	Ligand cell type string inputted by user, the name of the cell type should match the names in the `spot_cell_proportion_dataframe` during the `run_SpaCCI` analysis.
`celltype_receptor`	Receptor cell type string inputted by user, the name of the cell type should match the names in the `spot_cell_proportion_dataframe` during the `run_SpaCCI` analysis.
`plot_size`	As this function incorporate with `Seurat`'s `SpatialFeaturePlot`, this parameter could control the plotting size of the each spot.
`L_R_pair_name`	Initially this is set to `NULL`, if one is interested in a specific Ligand-Receptor pair, then one could specify the L_R_pair_name here. Note: the input name should match the L-R pair name exists in the dataframe in the output of SpaCCI_local "dataframelist".
`alpha`	This is the significant cutoff for the adjusted-p-value of thr permutation test. Initially this is set to `0.05`, one could adjust the cutoff.

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)


# 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)

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

Arguments

`SpaCCI_Result_List`	A list containing the results from a SpaCCI `"regional"` or `"global"` analysis. This list should include `pvalue_df`, which are the outputs from `run_SpaCCI(..., analysis_scale = "regional",...)` or `run_SpaCCI(..., analysis_scale = "global",...)`.
`specific_celltypes`	A character vector specifying the cell types to include in the plot, RECOMMEND using colnames of cell type proportion matrix to include all cell types. If `NULL`, cell types that involved in significant interactions are included.
`pathway_name`	A single character string specifying the pathway name to filter the interactions. If `NULL`, all pathways are included.
`L_R_pair_name`	A character vector specifying the ligand-receptor pair names to include in the plot. If `NULL`, all interactions are included.
`color`	A named vector of colors to use for the cell types. If `NULL`, a default color palette is used.
`alpha`	A numeric value specifying the significance threshold for adjusted P-values. Initially, set to `0.05`.

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")

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,
  ...
)
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

`SpaCCI_Result_List`	A list containing the results from a SpaCCI `"regional"` or `"global"` analysis. This list should include `pvalue_df`, which are the outputs from `run_SpaCCI(..., analysis_scale = "regional",...)` or `run_SpaCCI(..., analysis_scale = "global",...)`.
`specific_celltypes`	A vector of cell types to include in the heatmap, i.e c("Celltype_A","Celltype_B"). NOTE: the cell type names should match the names input in the SpaCCI analysis.
`pathways`	A vector of pathways to filter the interactions. Initially set to `NULL`, if not, then it will aggregate the results of the selected pathways.
`interaction`	A vector of interactions to filter. Initially set to `NULL`, if not, then it will aggregate the results of the selected interactions.
`log1p_transform`	Logical; whether to apply a log(1 + x) transformation to the count matrix.
`show_rownames`	Logical; whether to show row names in the heatmap.
`show_colnames`	Logical; whether to show column names in the heatmap.
`scale`	Character; whether to scale the data ("row", "column", "none").
`cluster_cols`	Logical; whether to cluster columns.
`cluster_rows`	Logical; whether to cluster rows.
`border_color`	Character; color of the heatmap borders.
`fontsize_row`	Numeric; font size for row names.
`fontsize_col`	Numeric; font size for column names.
`family`	Character; font family for text in the heatmap.
`main`	Character; title of the heatmap.
`treeheight_col`	Numeric; height of the column dendrogram.
`treeheight_row`	Numeric; height of the row dendrogram.
`low_col`	Character; color for low values in the heatmap.
`mid_col`	Character; color for mid values in the heatmap.
`high_col`	Character; color for high values in the heatmap.
`alpha`	Numeric; significance threshold for p-values, initailly set to `0.05`.
`return_tables`	Logical; whether to return the count matrix and summary tables.
`symmetrical`	Logical; whether to make the heatmap symmetrical.
`...`	Additional arguments passed to 'pheatmap'.

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")

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
)
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

`Seurat_Object`	Optional. A Seurat object containing spatial data. If provided, the function will plot the interaction patterns on the tissue image.
`spatial_coordinates_dataframe`	Optional. A data frame containing the spatial coordinates of the spots. The columns should include `"Spot_ID"`, `"imagerow"`, and `"imagecol"`. The row names must be the names of `"Spot_ID"`, matching those in the cell type proportion data frame or the gene expression data frame.
`SpaCCI_local_Result_List`	A list containing the results from a SpaCCI local analysis. This list should include `dataframelist` and `RegionIDs_matrix`, which are the outputs from `run_SpaCCI(..., analysis_scale = "local",...)`.
`Ligand_cell_type`	The name of the ligand cell type to plot. This should match the cell type names used in the `run_SpaCCI` analysis.
`Receptor_cell_type`	The name of the receptor cell type to plot. This should match the cell type names used in the `run_SpaCCI` analysis.
`spot_plot_size`	A numeric value controlling the size of the spots in the plot.
`specific_LR_pair_name`	Optional. The name of a specific ligand-receptor pair to plot. If provided, the plot will focus on this interaction. The name should match those in the `SpaCCI_local_Result_List$dataframelist`.
`significant_cutoff`	A numeric value specifying the significance cutoff for the adjusted P-values from the permutation test. Default is `0.05`.

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)
data(result_local)
data(test_data)
spatial_coords_df <- test_data$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 = "beta",
                  Receptor_cell_type = "delta",
                  spot_plot_size = 3)

# Plot localized SpaCCI results using Seurat object
library(SpaCCI)
data(result_local)
data(test_data)
spatial_coords_df <- test_data$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 = "beta",
                  Receptor_cell_type = "delta",
                  spot_plot_size = 3)

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
)
possible_L_R_pairs_cellchat(
  species,
  gene_spot_expression_dataframe,
  percentage
)

Arguments

`species`	A string specifying the species ("Human" or "Mouse"). The function selects the appropriate CellChatDB object, typically 'CellChatDB.human' or 'CellChatDB.mouse', which contains information on ligand-receptor interactions.
`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is 10.

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)

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

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
)
possible_L_R_pairs_Cellinker(
  species,
  gene_spot_expression_dataframe,
  percentage
)

Arguments

`species`	A string specifying the species ("Human" or "Mouse"). The function selects the appropriate Cellinker interaction file based on this input.
`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is 10.

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

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)
possible_L_R_pairs_cellphoneDB(gene_spot_expression_dataframe, percentage)

Arguments

`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is 10.

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)
possible_L_R_pairs_connectome(gene_spot_expression_dataframe, percentage)

Arguments

`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is 10.

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.

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)
possible_L_R_pairs_ICELLNET(gene_spot_expression_dataframe, percentage)

Arguments

`gene_spot_expression_dataframe`	A gene expression data frame with genes as row names and Spot IDs as column names. This data frame is used to verify the presence of all genes involved in the L-R pairs.
`percentage`	A numeric value specifying the minimum percentage of spots in which a gene must be expressed to be considered. The default is 10.

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.

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)
random_region(spatial_coord, center_id, n_ids)

Arguments

`spatial_coord`	A data frame of the spatial coordinates. The column names should include 'c("Spot_ID", "imagerow", "imagecol")', and the row names must be the Spot_IDs, which is the same as the row names in the cell type proportion data frame or the column names of the gene*spot expression data frame.
`center_id`	A character string specifying the ID of the center spot from which distances are calculated.
`n_ids`	An integer specifying the number of closest IDs to select.

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)

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)
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)
data(result_global)
  print(result_global)

result_local 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_local)
data(result_local)

Format

A list containing:

dataframelist: A list of data frame of the p-value reults of each spatial neighborhood.
RegionIDs_matrix: A list of matrix contains the spot IDs of each spatial neighborhood.

Details

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

Examples

  data(result_local)
  print(result_local)
data(result_local)
  print(result_local)

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
)
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

`gene_spot_expression_dataframe`	A data frame of gene expression values, where row names are genes and column names are spot IDs.
`spot_cell_proportion_dataframe`	A data frame of cell type proportions, where row names are spot IDs and column names are cell types.
`spatial_coordinates_dataframe`	A data frame containing the spatial coordinates of the spots. The columns should include `"Spot_ID"`, `"imagerow"`, and `"imagecol"`. And the row names must be the names of `"Spot_ID"`.
`LR_database_list`	A list containing ligand-receptor pairs and additional information, generated by functions using `LR_database()`.
`specific_LR_pair`	Required if `analysis_scale` is `"local"`. A vector of ligand-receptor pair names for localized analysis. The names should match those in row names in `LR_database_list$possible_LR_pairs_info`.
`analysis_scale`	A string specifying the scale of analysis: `"global"`, `"regional"`, or `"local"`.
`region_spot_IDs`	Required if `analysis_scale` is "regional". A vector of spot IDs defining the region for regional analysis.
`local_scale_proportion`	Optional. A numeric value ranging from 0 to 1, (0,1] specifying the proportion of spots to use for localized analysis. Default is `1`, meaning using `100`% proportion of spots. One could modified if want to reducing computing time.
`neighborhood_radius`	Optional. A numeric value specifying the radius of the neighborhood for localized analysis. Default is `2.5`, according to the 10X Visium ST data accounting for 200-250 $\mu$ m interacting distance.

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)
#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 = "EDN2_EDNRA",
                          analysis_scale = "local",
                          local_scale_proportion = 0.1,
                          neighborhood_radius = 2.5)

library(SpaCCI)
library(nnls)
#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 = "EDN2_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)
scPalette(n)

Arguments

`n`	An integer specifying the number of colors needed.

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)

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

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

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

Arguments

`gene_spot_df`	A data frame where the rows are genes and the columns are spots (Spot_IDs), representing gene expression levels across spatial spots.
`spot_cell_prop_df`	A data frame of cell type proportions for each spot. The rows represent spots (Spot_IDs), and the columns represent different cell types.
`matching_L_R_pairs`	A data frame containing matching ligand-receptor pairs. Each row corresponds to a ligand-receptor pair, with columns for `ligand_vector` and `receptor_vector`.
`matching_L_R_pairs_info`	A data frame providing additional information for each ligand-receptor pair, such as pathway information.

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

Arguments

`gene_spot_df`	A data frame where the rows are genes and the columns are spots (Spot_IDs), representing gene expression levels across spatial spots.
`spot_cell_prop_df`	A data frame of cell type proportions for each spot. The rows represent spots (Spot_IDs), and the columns represent different cell types.
`spatial_coord`	A data frame of the spatial coordinates. The column names should include 'c("Spot_ID", "imagerow", "imagecol")', and the row names must be the Spot_IDs, which is the same as the row names in the cell type proportion data frame or the column names of the gene*spot expression data frame.
`prop`	A numeric value representing the proportion of spots to randomly sample as center spots for local neighborhood analysis.
`radius`	A numeric value specifying the radius of the spatial neighborhood around each center spot.
`matching_L_R_pairs`	A data frame containing matching ligand-receptor pairs. Each row corresponds to a ligand-receptor pair, with columns for `ligand_vector` and `receptor_vector`.
`matching_L_R_pairs_info`	A data frame providing additional information for each ligand-receptor pair, such as pathway information.

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

Arguments

`gene_spot_df`	A data frame where the rows are genes and the columns are spots (Spot_IDs), representing gene expression levels across spatial spots.
`spot_cell_prop_df`	A data frame of cell type proportions for each spot. The rows represent spots (Spot_IDs), and the columns represent different cell types.
`region_spot_IDs`	A vector of Spot_IDs representing the spots included in the region of interest.
`matching_L_R_pairs`	A data frame containing matching ligand-receptor pairs. Each row corresponds to a ligand-receptor pair, with columns for `ligand_vector` and `receptor_vector`.
`matching_L_R_pairs_info`	A data frame providing additional information for each ligand-receptor pair, such as pathway information.

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.

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)
data(test_data)

Format

An object of class list of length 3.

Examples

library(SpaCCI)
data(test_data)
library(SpaCCI)
data(test_data)