Package 'FastCCCR' reference manual

Title:	Fast Cell-Cell Communication Analysis with Statistical Framework
Description:	A high-performance R package for cell-cell communication (CCC) analysis in single-cell RNA sequencing data. FastCCCR implements an innovative statistical framework based on exact null distribution computation, Cauchy combination of multiple statistical methods, and reference-based inference. The package is optimized with Rcpp for computational efficiency and supports both Seurat V4 and V5 objects. Key features include: (1) Multiple scoring methods with Cauchy combination, (2) Reference panel construction and query inference, (3) Support for multiple ligand-receptor databases (CellPhoneDB, CellChat, NicheNet), (4) High-performance vectorized and parallel computation.
Authors:	Zaoqu Liu [aut, cre] (ORCID: <https://orcid.org/0000-0002-5520-5765>), Wenjing Zhong [ctb], Marvin Yao [ctb]
Maintainer:	Zaoqu Liu <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.0
Built:	2026-05-26 08:19:42 UTC
Source:	https://github.com/Zaoqu-Liu/FastCCCR

FastCCCR: Fast Cell-Cell Communication Analysis

Description

A high-performance R package for cell-cell communication (CCC) analysis in single-cell RNA sequencing data. FastCCCR implements an innovative statistical framework based on exact null distribution computation, Cauchy combination of multiple statistical methods, and reference-based inference.

Main Functions

fastccc: Simple wrapper for CCC analysis
fastccc_cauchy: Full analysis with Cauchy combination
statistical_analysis_method: Single method analysis
build_reference: Build reference panel
infer_query: Infer using reference

Visualization

plot_ccc_network: Network visualization
plot_ccc_heatmap: Heatmap visualization
plot_ccc_chord: Chord diagram

Supported Databases

CellPhoneDB v4.1.0, v5.0.0
CellChat v1.6.1
NicheNet v1.1.1, v2.1.5

Seurat Compatibility

FastCCCR supports both Seurat V4 and V5 objects. The package automatically detects the Seurat version and uses the appropriate methods.

Author(s)

Zaoqu Liu [email protected]

References

Liu, Z. et al. (2026). FastCCCR: Fast Cell-Cell Communication Analysis with Statistical Framework.

Analyze Interaction Percentages

Description

Determine which interactions pass expression threshold

Usage

analyze_interactions_percents(
  cluster_percents,
  interactions,
  threshold = 0.1,
  sep = "|"
)
analyze_interactions_percents(
  cluster_percents,
  interactions,
  threshold = 0.1,
  sep = "|"
)

Arguments

cluster_percents

data.table of expression percentages

interactions

data.table of interactions

threshold

Minimum percentage threshold

sep

Separator for cell type pair names

Value

data.table of logical values (TRUE = passes threshold)

Batch compute gene sum distributions

Description

Compute sum distributions for all genes at once (FAST!)

Usage

batch_compute_gene_distributions_cpp(
  expr_mat,
  bin_edges_r,
  n_fft = 30L,
  max_value = 20,
  n_threads = 0L
)
batch_compute_gene_distributions_cpp(
  expr_mat,
  bin_edges_r,
  n_fft = 30L,
  max_value = 20,
  n_threads = 0L
)

Arguments

expr_mat

Expression matrix (genes x cells)

n_fft

Number of convolutions to compute

max_value

Maximum value to clip (for histogram)

n_threads

Number of threads (0 = auto)

bin_edges

Histogram bin edges

Value

List of lists: gene_sum_pmf[gene][n]

Batch compute p-values for L-R interactions

Description

Compute p-values for all valid interactions at once (FAST!)

Usage

batch_compute_pvalues_cpp(
  ligand_pmfs,
  receptor_pmfs,
  observed_values,
  precision = 0.01
)
batch_compute_pvalues_cpp(
  ligand_pmfs,
  receptor_pmfs,
  observed_values,
  precision = 0.01
)

Arguments

ligand_pmfs

List of ligand PMF arrays (one per interaction)

receptor_pmfs

List of receptor PMF arrays (one per interaction)

observed_values

Vector of observed interaction strengths

precision

PMF precision (default 0.01)

Value

Vector of p-values

Build Reference Panel

Description

Build a CCC reference panel from atlas data

Usage

build_reference(
  seurat_obj,
  reference_name,
  database,
  celltype_col,
  save_path = ".",
  min_percentile = 0.1,
  min_genes_per_cell = 50L,
  debug_mode = FALSE
)
build_reference(
  seurat_obj,
  reference_name,
  database,
  celltype_col,
  save_path = ".",
  min_percentile = 0.1,
  min_genes_per_cell = 50L,
  debug_mode = FALSE
)

Arguments

seurat_obj

Seurat object with reference data

reference_name

Name for the reference

database

Database name or path

celltype_col

Column name for cell type

save_path

Path to save reference

min_percentile

Minimum expression percentage

min_genes_per_cell

Minimum genes per cell for QC

debug_mode

Whether to save debug files

Value

Invisibly returns save path

Reference Building Functions

Description

Functions for building CCC reference panels

Calculate Cluster Mean Expression

Description

Calculate mean expression per cluster for each gene

Usage

calculate_cluster_mean(counts_dt, labels_dt)
calculate_cluster_mean(counts_dt, labels_dt)

Arguments

counts_dt

data.table of expression (cells x genes)

labels_dt

data.table with cell_type column

Value

data.table (clusters x genes)

Calculate Cluster Mean Distribution

Description

Compute null distribution of mean expression per cluster

Usage

calculate_cluster_mean_distribution(
  counts_dt,
  labels_dt,
  n_fft = NULL,
  mode = "continuous",
  parallel = FALSE,
  n_cores = NULL
)
calculate_cluster_mean_distribution(
  counts_dt,
  labels_dt,
  n_fft = NULL,
  mode = "continuous",
  parallel = FALSE,
  n_cores = NULL
)

Arguments

counts_dt

data.table of expression (cells x genes)

labels_dt

data.table with cell_type column

n_fft

Maximum n for exact convolution. NULL = auto (30 for continuous, 100 for digit)

mode

Distribution mode ("continuous" or "digit")

parallel

Whether to use parallel computation

n_cores

Number of cores for parallel

Value

data.table where each cell contains a Distribution object

Calculate cluster means (vectorized)

Description

Calculate mean expression per cluster

Usage

calculate_cluster_means_cpp(expr, labels, n_clusters)
calculate_cluster_means_cpp(expr, labels, n_clusters)

Arguments

expr

Expression matrix (genes x cells)

labels

Integer vector of cluster labels (1-indexed)

n_clusters

Number of clusters

Value

Matrix of means (genes x clusters)

Calculate Cluster Expression Percentages

Description

Calculate percentage of cells expressing each gene per cluster

Usage

calculate_cluster_percents(counts_dt, labels_dt)
calculate_cluster_percents(counts_dt, labels_dt)

Arguments

counts_dt

data.table of expression

labels_dt

data.table with cell_type column

Value

data.table (clusters x genes)

Calculate cluster expression percentages

Description

Calculate percentage of cells expressing each gene per cluster

Usage

calculate_cluster_percents_cpp(expr, labels, n_clusters)
calculate_cluster_percents_cpp(expr, labels, n_clusters)

Arguments

expr

Expression matrix (genes x cells)

labels

Integer vector of cluster labels (1-indexed)

n_clusters

Number of clusters

Value

Matrix of percentages (genes x clusters)

Calculate Cluster Quantile Expression

Description

Calculate quantile expression per cluster

Usage

calculate_cluster_quantile(counts_dt, labels_dt, qt = 0.9)
calculate_cluster_quantile(counts_dt, labels_dt, qt = 0.9)

Arguments

counts_dt

data.table of expression

labels_dt

data.table with cell_type column

qt

Quantile (0-1)

Value

data.table (clusters x genes)

Calculate Cluster Quantile Distribution

Description

Compute null distribution of quantile expression per cluster

Usage

calculate_cluster_quantile_distribution(
  counts_dt,
  labels_dt,
  quantile = 0.9,
  mode = "continuous",
  parallel = FALSE,
  n_cores = NULL
)
calculate_cluster_quantile_distribution(
  counts_dt,
  labels_dt,
  quantile = 0.9,
  mode = "continuous",
  parallel = FALSE,
  n_cores = NULL
)

Arguments

counts_dt

data.table of expression

labels_dt

data.table with cell_type column

quantile

Quantile to compute (0-1)

mode

Distribution mode ("continuous" or "digit")

parallel

Whether to use parallel computation

n_cores

Number of cores

Value

data.table where each cell contains a Distribution object

Calculate cluster quantiles (vectorized)

Description

Calculate quantile expression per cluster

Usage

calculate_cluster_quantiles_cpp(expr, labels, n_clusters, quantile = 0.9)
calculate_cluster_quantiles_cpp(expr, labels, n_clusters, quantile = 0.9)

Arguments

expr

Expression matrix (genes x cells)

labels

Integer vector of cluster labels (1-indexed)

n_clusters

Number of clusters

quantile

Quantile to compute (0-1)

Value

Matrix of quantiles (genes x clusters)

Calculate interaction strengths (vectorized)

Description

Calculate L-R interaction strengths for all cell type pairs

Usage

calculate_interaction_strengths_cpp(
  mean_expr,
  ligand_idx,
  receptor_idx,
  method = 1L
)
calculate_interaction_strengths_cpp(
  mean_expr,
  ligand_idx,
  receptor_idx,
  method = 1L
)

Arguments

mean_expr

Mean expression matrix (genes x clusters)

ligand_idx

Ligand gene indices (0-indexed)

receptor_idx

Receptor gene indices (0-indexed)

method

Method: 1 = arithmetic, 2 = geometric

Value

3D array (n_interactions x n_clusters x n_clusters)

Calculate Interaction P-values

Description

Compute p-values for L-R interactions

Usage

calculate_interactions_pvalue(
  mean_pmf_dt,
  interactions,
  interactions_strength,
  percents_analysis,
  method = "Arithmetic",
  parallel = FALSE,
  n_cores = NULL
)
calculate_interactions_pvalue(
  mean_pmf_dt,
  interactions,
  interactions_strength,
  percents_analysis,
  method = "Arithmetic",
  parallel = FALSE,
  n_cores = NULL
)

Arguments

mean_pmf_dt

data.table of cluster distributions (clusters x genes)

interactions

data.table of interactions

interactions_strength

data.table of interaction strengths

percents_analysis

data.table of logical (passes threshold)

method

"Arithmetic" or "Geometric"

parallel

Whether to use parallel processing (reserved for future)

n_cores

Number of cores for parallel processing (reserved for future)

Value

data.table of p-values (cell type pairs x interactions)

Calculate Interaction Strengths

Description

Calculate L-R interaction strengths for all cell type pairs. Uses C++ implementation for ~20x speedup.

Usage

calculate_interactions_strength(
  mean_counts,
  interactions,
  method = "Arithmetic",
  sep = "|"
)
calculate_interactions_strength(
  mean_counts,
  interactions,
  method = "Arithmetic",
  sep = "|"
)

Arguments

mean_counts

data.table of mean expression (clusters x genes)

interactions

data.table of interactions

method

"Arithmetic" or "Geometric"

sep

Separator for cell type pair names

Value

data.table (cell type pairs x interactions)

Cauchy Combination of P-values

Description

Combine p-values from multiple methods using Cauchy distribution

Usage

cauchy_combine(pval_list, weights = NULL)
cauchy_combine(pval_list, weights = NULL)

Arguments

pval_list

List of p-value data.tables

weights

Optional weight vector (NULL for equal weights)

Value

data.table of combined p-values

Cauchy combination of p-values

Description

Combine p-values using Cauchy distribution

Usage

cauchy_combine_cpp(pvals, weights = NULL)
cauchy_combine_cpp(pvals, weights = NULL)

Arguments

pvals

Matrix of p-values (rows = tests, cols = methods)

weights

Weight vector (NULL for equal weights)

Value

Vector of combined p-values

Cauchy Combine from Files

Description

Combine p-value files in a directory

Usage

cauchy_combine_files(result_dir, task_id = NULL, pattern = "*_pvals.tsv")
cauchy_combine_files(result_dir, task_id = NULL, pattern = "*_pvals.tsv")

Arguments

result_dir

Directory containing p-value files

task_id

Task ID to filter files (NULL for all)

pattern

File pattern to match

Value

data.table of combined p-values

Cauchy Combine with Statistic

Description

Combine p-values and compute Cauchy statistic

Usage

cauchy_combine_with_stat(pval_list, weights = NULL)
cauchy_combine_with_stat(pval_list, weights = NULL)

Arguments

pval_list

List of p-value data.tables

weights

Optional weight vector

Value

List with combined p-values and statistics

Cauchy Combination Functions

Description

Functions for combining p-values using Cauchy distribution

Check Interactions by DEG

Description

Additional filtering based on differential expression

Usage

check_interactions_by_DEG(mean_counts, mean_pmf_dt, interactions, pvals)
check_interactions_by_DEG(mean_counts, mean_pmf_dt, interactions, pvals)

Arguments

mean_counts

data.table of mean expression

mean_pmf_dt

data.table of distributions

interactions

data.table of interactions

pvals

data.table of p-values

Value

Updated p-values with DEG filtering

Cluster Markers Method

Description

Calculate cluster markers using FastCCC statistics

Usage

cluster_markers_method(
  seurat_obj,
  celltype_col,
  cluster_method = "Mean",
  quantile = 0.9
)
cluster_markers_method(
  seurat_obj,
  celltype_col,
  cluster_method = "Mean",
  quantile = 0.9
)

Arguments

seurat_obj

Seurat object

celltype_col

Column for cell type

cluster_method

Method ("Mean" or "Quantile")

quantile

Quantile value

Value

List with mean counts, distributions, and p-values

Combine Complex Distribution

Description

Add complex expression scores to cluster scores

Usage

combine_complex_distribution(mean_counts, complex_table, agg_func = "min")
combine_complex_distribution(mean_counts, complex_table, agg_func = "min")

Arguments

mean_counts

data.table of cluster means

complex_table

data.table of complex composition

agg_func

Aggregation function ("min" or "mean")

Value

data.table with complex scores added

Combine Complex Distribution

Description

Add complex distributions to cluster distributions

Usage

combine_complex_distribution_dt(dist_dt, complex_table, complex_func = "min")
combine_complex_distribution_dt(dist_dt, complex_table, complex_func = "min")

Arguments

dist_dt

data.table of distributions (clusters x genes)

complex_table

data.table of complex composition

complex_func

Function to combine subunit distributions ("min" or "avg")

Value

data.table with complex distributions added

Core Functions for FastCCCR

Description

Main entry functions for cell-cell communication analysis

Create Distribution from Samples

Description

Create Distribution from Samples

Usage

create_distribution(samples, mode = "continuous")
create_distribution(samples, mode = "continuous")

Arguments

samples

Numeric vector of samples

mode

"continuous" or "digit"

Value

Distribution object

Create Significant Interactions DataFrame

Description

Create summary of significant interactions

Usage

create_significant_df(pvals, database_path, threshold = 0.05)
create_significant_df(pvals, database_path, threshold = 0.05)

Arguments

pvals

data.table of p-values

database_path

Path to database

threshold

P-value threshold

Value

data.table of significant interactions

Data Preprocessing Functions for FastCCCR

Description

Functions for loading and preprocessing data

Digitize transform for rank-based analysis

Description

Transform expression values to rank-based bins

Usage

digitize_transform_cpp(x, n_bins = 50L)
digitize_transform_cpp(x, n_bins = 50L)

Arguments

x

Numeric vector (non-zero values)

n_bins

Number of bins

Value

Integer vector of bin indices

Complex Distribution Functions

Description

Functions for computing distributions of protein complexes

IID Set Distribution Functions

Description

Functions for computing distributions of cluster statistics

L-R Distribution and P-value Functions

Description

Functions for computing L-R interaction p-values

Distribution Class for FastCCCR

Description

R6 class representing probability distributions for CCC analysis

An R6 class representing probability mass functions (PMF) with optimized operations for cell-cell communication analysis. Supports both discrete PMF arrays and normal distribution approximations.

Details

The Distribution class supports the following operations:

Addition: Sum of two independent random variables (FFT convolution)
Power: Sum of n i.i.d. random variables
Division: Average of n i.i.d. random variables
And (&): Special operation for L+R combination

Public fields

dtype: Distribution type: "normal" or "other"
loc: Location parameter (mean for normal)
scale: Scale parameter (sd for normal)
pmf_array: PMF array
is_align: Whether PMF is aligned to standard bins
is_normal_type: Whether distribution is normal type
is_analytic: Whether distribution has analytic CDF
is_complex_analytic: Whether from complex combination
cdf_analytic_func: Analytic CDF function (for normal)
min_cdf_non_zero: Lower bound of support
min_cdf_one: Upper bound of support
support_length: Length of support
ligand: Ligand distribution (for complex analytic)
receptor: Receptor distribution (for complex analytic)
mode: Distribution mode: "continuous" or "digit"

Methods

Method `new()`

Create a new Distribution object

Usage

Distribution$new(
  dtype = "other",
  loc = NULL,
  scale = NULL,
  samples = NULL,
  pmf_array = NULL,
  is_align = NULL,
  mode = "continuous",
  eps = 1e-04
)

Arguments

dtype: Type: "normal", "gaussian", or "other"
loc: Location parameter (mean)
scale: Scale parameter (sd)
samples: Sample data to construct PMF
pmf_array: Pre-computed PMF array
is_align: Whether PMF is aligned
mode: Distribution mode ("continuous" or "digit")
eps: Tolerance for PMF sum check

Returns

A new Distribution object

Method `get_mean()`

Get mean of distribution

Usage

Distribution$get_mean()

Returns

Mean value

Method `get_std()`

Get standard deviation

Usage

Distribution$get_std()

Returns

SD value

Method `get_var()`

Get variance

Usage

Distribution$get_var()

Returns

Variance value

Method `get_pmf_array()`

Get PMF array

Usage

Distribution$get_pmf_array()

Returns

PMF array

Method `get_cdf_array()`

Get CDF array

Usage

Distribution$get_cdf_array()

Returns

CDF array

Method `add()`

Add two distributions (convolution)

Usage

Distribution$add(other)

Arguments

other: Another Distribution object

Returns

New Distribution representing sum

Method `power()`

Sum of n i.i.d. copies

Usage

Distribution$power(n)

Arguments

n: Number of copies

Returns

New Distribution

Method `divide()`

Average of n i.i.d. copies

Usage

Distribution$divide(n)

Arguments

n: Number of copies

Returns

New Distribution

Method `and_op()`

Special "and" operation for L+R

Usage

Distribution$and_op(other)

Arguments

other: Another Distribution

Returns

New Distribution

Method `mul_op()`

Geometric mean of two distributions (sqrt(X*Y))

Usage

Distribution$mul_op(other)

Arguments

other: Another Distribution

Details

Uses C++ implementation for ~80x speedup over R version. Computes distribution of sqrt(X*Y) given distributions of X and Y.

Returns

New Distribution representing sqrt(XY)

Method `copy()`

Copy the distribution

Usage

Distribution$copy()

Returns

A deep copy of this Distribution

Method `print()`

Print method

Usage

Distribution$print()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Distribution$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Fast histogram for PMF computation

Description

Compute histogram with specified breaks

Usage

fast_histogram_cpp(x, breaks)
fast_histogram_cpp(x, breaks)

Arguments

x

Numeric vector

breaks

Break points

Value

Counts per bin

Simplified FastCCC Function

Description

Simple wrapper for FastCCC analysis

Usage

fastccc(seurat_obj, database = "CPDBv5.0.0", celltype_col = "cell_type", ...)
fastccc(seurat_obj, database = "CPDBv5.0.0", celltype_col = "cell_type", ...)

Arguments

seurat_obj

Seurat object

database

Database name (default: "CPDBv5.0.0")

celltype_col

Cell type column name

...

Additional arguments passed to fastccc_cauchy

Value

List with analysis results

FastCCC with Cauchy Combination

Description

Run FastCCC analysis with multiple methods and Cauchy combination

Usage

fastccc_cauchy(
  seurat_obj,
  database,
  celltype_col,
  single_unit_summary_list = c("Mean", "Median", "Q3", "Quantile_0.9"),
  complex_aggregation_list = c("Minimum", "Average"),
  LR_combination_list = c("Arithmetic", "Geometric"),
  min_percentile = 0.1,
  save_path = NULL,
  use_DEG = FALSE,
  parallel = FALSE,
  n_cores = NULL
)
fastccc_cauchy(
  seurat_obj,
  database,
  celltype_col,
  single_unit_summary_list = c("Mean", "Median", "Q3", "Quantile_0.9"),
  complex_aggregation_list = c("Minimum", "Average"),
  LR_combination_list = c("Arithmetic", "Geometric"),
  min_percentile = 0.1,
  save_path = NULL,
  use_DEG = FALSE,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

seurat_obj

Seurat object

database

Database name or path

celltype_col

Column name for cell type labels

single_unit_summary_list

Methods for single-unit summary

complex_aggregation_list

Methods for complex aggregation

LR_combination_list

Methods for L-R combination

min_percentile

Minimum expression percentage threshold

save_path

Path to save results (NULL for current directory)

use_DEG

Whether to filter by DEG

parallel

Whether to use parallel computation

n_cores

Number of cores for parallel

Value

List with results

FFT Convolution

Description

Fast convolution using FFT

Usage

fft_convolve_cpp(x, y)
fft_convolve_cpp(x, y)

Arguments

x

First vector

y

Second vector

Value

Convolution result

Get Average Distribution

Description

Compute distribution of average of k random variables

Usage

get_average_distribution(dist_list)
get_average_distribution(dist_list)

Arguments

dist_list

List of Distribution objects

Value

Distribution object

Get Count Data Only

Description

Extract expression data without interaction filtering

Usage

get_count_data(seurat_obj, celltype_col)
get_count_data(seurat_obj, celltype_col)

Arguments

seurat_obj

Seurat object

celltype_col

Column name for cell type

Value

List with counts_dt and labels_dt

Get Input Data from Seurat Object

Description

Extract and preprocess data from Seurat object for CCC analysis

Usage

get_input_data(
  seurat_obj,
  database,
  celltype_col,
  convert_type = "hgnc_symbol",
  filter_genes = FALSE,
  min_cells = 1L
)
get_input_data(
  seurat_obj,
  database,
  celltype_col,
  convert_type = "hgnc_symbol",
  filter_genes = FALSE,
  min_cells = 1L
)

Arguments

seurat_obj

Seurat object

database

Database name or path

celltype_col

Column name for cell type labels

convert_type

Gene name type ("hgnc_symbol")

filter_genes

Whether to filter low-expressed genes

min_cells

Minimum cells expressing a gene

Value

List containing counts_dt, labels_dt, complex_table, interactions

Load Data from Files

Description

Load expression and metadata from files

Usage

get_input_data_from_files(
  counts_file,
  meta_file,
  database,
  celltype_col = "cell_type",
  convert_type = "hgnc_symbol"
)
get_input_data_from_files(
  counts_file,
  meta_file,
  database,
  celltype_col = "cell_type",
  convert_type = "hgnc_symbol"
)

Arguments

counts_file

Path to counts file (CSV/TSV)

meta_file

Path to metadata file

database

Database name or path

celltype_col

Column name for cell type

convert_type

Gene name type

Value

List with processed data

Get Interactions from Database

Description

Load ligand-receptor interactions from database

Usage

get_interactions(database, select_list = NULL)
get_interactions(database, select_list = NULL)

Arguments

database

Database name or path

select_list

Optional vector of interaction IDs to select

Value

data.table with interaction information

Get Minimum Distribution

Description

Get Minimum Distribution

Usage

get_minimum_distribution(dist_list)
get_minimum_distribution(dist_list)

Arguments

dist_list

List of Distribution objects

Value

Distribution object

Minimum Distribution

Description

Compute distribution of min(X1, X2, ..., Xk)

Usage

get_minimum_distribution_cpp(pmf_list)
get_minimum_distribution_cpp(pmf_list)

Arguments

pmf_list

List of PMF vectors

Value

PMF of minimum

Get Network Data

Description

Extract network data from results

Usage

get_network_data(results, pval_threshold = 0.05)
get_network_data(results, pval_threshold = 0.05)

Arguments

results

Results from fastccc

pval_threshold

P-value threshold

Value

List with nodes and edges data frames

Get PMF Array from Digitized Samples

Description

Get PMF Array from Digitized Samples

Usage

get_pmf_from_digit_samples(samples, n_bins = 50L)
get_pmf_from_digit_samples(samples, n_bins = 50L)

Arguments

samples

Digitized samples (integer 0 to n_bins)

n_bins

Number of bins

Value

PMF array

Get p-value from Distribution

Description

Get p-value from Distribution

Usage

get_pvalue(value, dist, mode = "continuous")
get_pvalue(value, dist, mode = "continuous")

Arguments

value

Observed value

dist

Distribution object or PMF array

mode

"continuous" or "digit"

Value

P-value

Calculate P-values from PMF (vectorized)

Description

Calculate p-values for multiple observed values

Usage

get_pvalues_from_pmf_cpp(values, pmf, precision = 0.01)
get_pvalues_from_pmf_cpp(values, pmf, precision = 0.01)

Arguments

values

Numeric vector of observed values

pmf

PMF array

precision

Precision (bin width)

Value

Vector of p-values

Get Strength Network Data

Description

Extract network data based on interaction strength

Usage

get_strength_network_data(results, pval_threshold = 0.05)
get_strength_network_data(results, pval_threshold = 0.05)

Arguments

results

Results containing both pvals and strength

pval_threshold

P-value threshold

Value

List with nodes and edges

Get threshold from Distribution (for significance)

Description

Get threshold from Distribution (for significance)

Usage

get_threshold(dist, alpha = 0.05, mode = "digit")
get_threshold(dist, alpha = 0.05, mode = "digit")

Arguments

dist

Distribution object

alpha

Significance level (right-tail area)

mode

"continuous" or "digit"

Value

Threshold value

Infer Query Using Reference

Description

Infer cell-cell communication for query data using reference

Usage

infer_query(
  seurat_obj,
  reference_path,
  database,
  celltype_col,
  celltype_mapping = NULL,
  save_path = NULL,
  min_genes_per_cell = 50L,
  debug_mode = FALSE,
  k = NULL
)
infer_query(
  seurat_obj,
  reference_path,
  database,
  celltype_col,
  celltype_mapping = NULL,
  save_path = NULL,
  min_genes_per_cell = 50L,
  debug_mode = FALSE,
  k = NULL
)

Arguments

seurat_obj

Query Seurat object

reference_path

Path to reference panel

database

Database name or path

celltype_col

Column name for cell type

celltype_mapping

Optional named list mapping reference to query cell types

save_path

Path to save results

min_genes_per_cell

Minimum genes per cell

debug_mode

Whether to output debug files

k

Scaling factor for confidence interval (NULL for auto)

Value

List with inference results

Query Inference Functions

Description

Functions for inferring CCC using reference panels

Load Reference Config

Description

Load Reference Config

Usage

load_reference_config(ref_path)
load_reference_config(ref_path)

Arguments

ref_path

Reference path

Value

List with configuration

Multiply Distributions

Description

Compute distribution of sqrt(X*Y) given distributions of X and Y

Usage

multiply_distributions_cpp(pmf1, pmf2)
multiply_distributions_cpp(pmf1, pmf2)

Arguments

pmf1

PMF of first distribution

pmf2

PMF of second distribution

Value

PMF of sqrt(X*Y)

Plot CCC Chord Diagram

Description

Create chord diagram of cell-cell communication

Usage

plot_ccc_chord(results, pval_threshold = 0.05, min_interactions = 1, ...)
plot_ccc_chord(results, pval_threshold = 0.05, min_interactions = 1, ...)

Arguments

results

Results from fastccc

pval_threshold

P-value threshold

min_interactions

Minimum interactions for connection

...

Additional arguments passed to circlize::chordDiagram

Value

NULL (plots to device)

Plot CCC Heatmap

Description

Create heatmap visualization of interaction strengths

Usage

plot_ccc_heatmap(
  results,
  show_pval = TRUE,
  pval_threshold = 0.05,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  top_n = 50,
  ...
)
plot_ccc_heatmap(
  results,
  show_pval = TRUE,
  pval_threshold = 0.05,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  top_n = 50,
  ...
)

Arguments

results

Results from fastccc

show_pval

Whether to overlay p-value significance

pval_threshold

P-value threshold

cluster_rows

Whether to cluster rows

cluster_cols

Whether to cluster columns

top_n

Show only top N interactions by variance

...

Additional arguments passed to ComplexHeatmap

Value

ComplexHeatmap object

Plot CCC Network

Description

Create network visualization of cell-cell communication

Usage

plot_ccc_network(
  results,
  pval_threshold = 0.05,
  min_interactions = 1,
  layout = "circle",
  vertex_size_scale = 1,
  edge_width_scale = 1,
  ...
)
plot_ccc_network(
  results,
  pval_threshold = 0.05,
  min_interactions = 1,
  layout = "circle",
  vertex_size_scale = 1,
  edge_width_scale = 1,
  ...
)

Arguments

results

Results from fastccc or related functions

pval_threshold

P-value threshold for significance

min_interactions

Minimum interactions to show edge

layout

Network layout algorithm

vertex_size_scale

Scale factor for vertex size

edge_width_scale

Scale factor for edge width

...

Additional arguments passed to plot

Value

igraph plot (invisibly returns igraph object)

Score Calculation Functions for FastCCCR

Description

Functions for calculating expression scores

FastCCC Single Method

Description

Run FastCCC with a single statistical method

Usage

statistical_analysis_method(
  seurat_obj,
  database,
  celltype_col,
  single_unit_summary = "Mean",
  complex_aggregation = "Minimum",
  LR_combination = "Arithmetic",
  min_percentile = 0.1,
  style = NULL,
  use_DEG = FALSE,
  save_path = NULL,
  parallel = FALSE,
  n_cores = NULL
)
statistical_analysis_method(
  seurat_obj,
  database,
  celltype_col,
  single_unit_summary = "Mean",
  complex_aggregation = "Minimum",
  LR_combination = "Arithmetic",
  min_percentile = 0.1,
  style = NULL,
  use_DEG = FALSE,
  save_path = NULL,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

seurat_obj

Seurat object

database

Database name or path

celltype_col

Column name for cell type labels

single_unit_summary

Single-unit summary method

complex_aggregation

Complex aggregation method

LR_combination

L-R combination method

min_percentile

Minimum expression percentage

style

Preset style ("cpdb" for CellPhoneDB-like)

use_DEG

Whether to filter by DEG

save_path

Path to save results

parallel

Whether to use parallel

n_cores

Number of cores

Value

List with results

Sum Distributions (for average)

Description

Compute sum of k distributions using repeated convolution

Usage

sum_distributions_cpp(pmf_list)
sum_distributions_cpp(pmf_list)

Arguments

pmf_list

List of PMF vectors

Value

PMF of sum

Visualization Functions for FastCCCR

Description

Functions for visualizing CCC analysis results

Package 'FastCCCR'

Help Index

FastCCCR: Fast Cell-Cell Communication Analysis

Description

Main Functions

Visualization

Supported Databases

Seurat Compatibility

Author(s)

References

See Also

Analyze Interaction Percentages

Description

Usage

Arguments

Value

Batch compute gene sum distributions

Description

Usage

Arguments

Value

Batch compute p-values for L-R interactions

Description

Usage

Arguments

Value

Build Reference Panel

Description

Usage

Arguments

Value

Reference Building Functions

Description

Calculate Cluster Mean Expression

Description

Usage

Arguments

Value

Calculate Cluster Mean Distribution

Description

Usage

Arguments

Value

Calculate cluster means (vectorized)

Description

Usage

Arguments

Value

Calculate Cluster Expression Percentages

Description

Usage

Arguments

Value

Calculate cluster expression percentages

Description

Usage

Arguments

Value

Calculate Cluster Quantile Expression

Description

Usage

Arguments

Value

Calculate Cluster Quantile Distribution

Description

Usage

Arguments

Value

Calculate cluster quantiles (vectorized)

Description

Usage

Arguments

Value

Calculate interaction strengths (vectorized)

Description

Usage

Arguments

Value

Calculate Interaction P-values

Description