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CellChat2 Pipeline Specification

Pipeline Details

  • Name: CellChat2
  • Pipeline UUID: 2e7e6bdfa3e442f489cfa57a63cb529b
  • Version: 1.0.1
  • View Pipeline:

Overview

CellChat2 pipeline is designed for inferring, visualizing, and analyzing cell-cell communication networks from single-cell RNA-seq and spatial transcriptomics data. It automates the systematic inference of signaling interactions between cell populations using curated ligand-receptor pairs and provides comprehensive comparative analysis capabilities across multiple biological conditions.

Key Use cases:

  • Comparative Analysis Across Conditions: Comparing inferred communication networks between different biological conditions (e.g., nonlesional vs. lesional skin) to identify conserved pathways, context-specific signaling, and major network rewiring.
  • Signaling Role Identification: Applying network centrality metrics (e.g., in-degree, out-degree, betweenness) to predict dominant signal senders, receivers, mediators, and influencers within communication networks.
  • Single-cell Communication Network Analysis: Systematic inference of cell-cell communication patterns from single-cell and spatially resolved transcriptomic datasets.

Features

  • Expanded Ligand-Receptor Database: Incorporates an enriched set of molecular interactions, including multimeric complexes and cofactors, with rich functional annotations to improve prediction accuracy.
  • Multi-Modal Data Support: Supports both single-cell RNA-seq and spatial transcriptomics data analysis.
  • Advanced Network Analysis: Employs social network analysis, pattern recognition, and manifold learning approaches to quantify communication strength and identify major signaling sources/targets.
  • Comparative Analysis: Enables comparison of communication networks across multiple biological conditions to identify conserved and context-specific signaling patterns.
  • Interactive Visualization: Generates two R Shiny applications with comprehensive plots in Rmarkdown format for interactive exploration of results.
  • Protein-Protein Interaction Integration: Optional projection of gene expression data onto PPI networks to reduce dropout effects in shallow sequencing data.
  • Containerized Environment: Runs in Docker container (quay.io/viascientific/cellchat2:2.0.0) ensuring reproducibility.

Input/Output Specification

Inputs

Required

rds_file

  • Description: Two rds files (e.g., untreated vs treated) with processed Seurat object, including cell type annotations. Name of the file should be in the format of {sample_name}.rds. The files should be given as collection.
  • Format: .rds
  • Example File Path: /samples/ctrl.rds

Optional Inputs

Organism

  • Description: Organism the samples belong to.
  • Available Options: Human and Mouse

Cell labels

  • Description: Column name of the cell type annotations in the meta data.

Ligand-receptor type

  • Description: Subset CellChatDB database by only including interactions of interest.
  • Options:
  • except_nonprotein: Use all CellChatDB except for "Non-protein Signaling"
  • all: Use all of CellChatDB

Project expression data onto PPI network

  • Description: Use the function to project gene expression data onto protein-protein interaction (PPI) network. A diffusion process is used to smooth genes' expression values based on their neighbors' defined in a high-confidence experimentally validated protein-protein network.
  • Purpose: Useful when analyzing single-cell data with shallow sequencing depth because the projection reduces the dropout effects of signaling genes.

Method

  • Description: Method for calculating the average gene expression per cell group.

Trim

  • Description: The fraction (0 to 0.25) of observations to be trimmed from each end of x before the mean is computed.

Min. number of cells

  • Description: The minimum number of cells required in each cell group for cell-cell communication.

Subset cell groups

  • Description: Subset to certain cell groups. Used in aggregateNet() function.

Outputs

Reported Outputs

  • single_sample_analysis:
  • Description: Rmarkdown style R shiny app for cell-cell communication analysis for each sample.
  • Format: .rds
  • Example File Path: /stim_cellchat.rds
  • Visualization App: CellChat2-signalling

  • Location: single_sample_analysis

  • comparison_analysis_result:

  • Description: Rmarkdown style R shiny app for comparison analysis of multiple samples.
  • Format: .rds
  • Example File Path: /report_input_files/stim_cellchat.rds
  • Visualization App: CellChat2
  • Location: comparison_analysis_result

Associated Processes

References & Additional Documentation

  • Example Datasets:
  • Example 1. PBMCs from SeuratData package (originally from Kang et al, 2017)
  • Source: http://seurat.nygenome.org/src/contrib/ifnb.SeuratData_3.1.0.tar.gz
  • Dataset: https://www.viafoundry.com/test_data/cellchat2/stim.rds (stimulation)
  • https://www.viafoundry.com/test_data/cellchat2/ctrl.rds (control)