Introduction to Connectome

Overview

Connectome is a computational framework for inferring and analyzing cell-cell communication networks from single-cell RNA sequencing (scRNA-seq) data. The package constructs connectomic edgelists that represent intercellular signaling topologies based on ligand-receptor co-expression patterns.

Key Features

Multi-species support: Human, mouse, rat, and pig via FANTOM5 database
Statistical rigor: Wilcoxon rank-sum tests with multiple testing correction
Network analysis: Kleinberg hub/authority scores for centrality analysis
Rich visualization: Network plots, circos diagrams, dot plots
Differential analysis: Compare connectivity between conditions

Installation

From R-universe (Recommended)

install.packages("Connectome", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub

remotes::install_github("Zaoqu-Liu/Connectome")

Quick Start

Load Required Packages

library(Seurat)
library(Connectome)

Prepare Your Data

Connectome requires a Seurat object with:

Normalized data (NormalizeData())
Scaled data (ScaleData())
Cell identity labels (stored in Idents())

# Example workflow
seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
Idents(seurat_obj) <- "cell_type"  # Set your cell type column

Create Connectome

connectome <- CreateConnectome(
  object = seurat_obj,
  species = "human",
  LR.database = "fantom5",
  min.cells.per.ident = 50,
  p.values = TRUE
)

Filter Edges

connectome_filtered <- FilterConnectome(
  connectome,
  min.pct = 0.1,       # Minimum expression fraction
  min.z = 0.25,        # Minimum z-score
  max.p = 0.05         # Maximum adjusted p-value
)

Visualize Results

Network Plot

NetworkPlot(connectome_filtered)

Example network plot showing cell-cell communication topology.

Circos Plot

CircosPlot(connectome_filtered)

Example circos plot showing ligand-receptor interactions.

Centrality Analysis

Centrality(connectome_filtered)

Example centrality analysis showing hub and authority scores.

Understanding the Output

The connectome is a data.frame where each row represents a potential signaling edge:

Column	Description
`source`	Sending cell population
`target`	Receiving cell population
`ligand`	Ligand gene symbol
`receptor`	Receptor gene symbol
`pair`	Ligand-Receptor pair identifier
`mode`	Signaling family/category
`ligand.expression`	Log-normalized ligand expression
`recept.expression`	Log-normalized receptor expression
`ligand.scale`	Z-scored ligand expression
`recept.scale`	Z-scored receptor expression
`percent.source`	Fraction of source cells expressing ligand
`percent.target`	Fraction of target cells expressing receptor
`weight_norm`	Edge weight (normalized expression)
`weight_sc`	Edge weight (scaled expression)
`p_val_adj.lig`	Adjusted p-value for ligand
`p_val_adj.rec`	Adjusted p-value for receptor

Workflow Summary

┌─────────────────────────────────────────────────────────────┐
│                     Seurat Object                            │
│  (Normalized + Scaled + Cell Type Labels)                    │
└─────────────────────┬───────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────────┐
│               CreateConnectome()                             │
│  • Extract expression matrices                               │
│  • Match ligand-receptor pairs from FANTOM5                  │
│  • Calculate edge weights                                    │
│  • Compute statistical significance (optional)               │
└─────────────────────┬───────────────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────────────┐
│               FilterConnectome()                             │
│  • Apply expression thresholds                               │
│  • Filter by significance                                    │
│  • Select signaling modes                                    │
└─────────────────────┬───────────────────────────────────────┘
                      │
          ┌───────────┴───────────┐
          ▼                       ▼
┌─────────────────────┐  ┌─────────────────────┐
│    Visualization    │  │      Analysis       │
│  • NetworkPlot()    │  │  • Centrality()     │
│  • CircosPlot()     │  │  • DOR()            │
│  • EdgeDotPlot()    │  │  • Differential     │
└─────────────────────┘  └─────────────────────┘

Next Steps

Algorithm Principles: Mathematical framework and theory
Visualization Gallery: Comprehensive visualization examples
Differential Analysis: Compare conditions
Best Practices: Troubleshooting and optimization

Citation

If you use Connectome in your research, please cite:

Raredon, M.S.B., Yang, J., Garritano, J. et al. Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome. Scientific Reports 12, 4187 (2022). https://doi.org/10.1038/s41598-022-07959-x

Session Info

sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 15.6.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] C
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39     desc_1.4.3        R6_2.6.1          fastmap_1.2.0    
#>  [5] xfun_0.56         cachem_1.1.0      knitr_1.51        htmltools_0.5.9  
#>  [9] rmarkdown_2.30    lifecycle_1.0.5   cli_3.6.5         sass_0.4.10      
#> [13] pkgdown_2.1.3     textshaping_1.0.4 jquerylib_0.1.4   systemfonts_1.3.1
#> [17] compiler_4.4.0    tools_4.4.0       ragg_1.5.0        bslib_0.9.0      
#> [21] evaluate_1.0.5    yaml_2.3.12       otel_0.2.0        jsonlite_2.0.0   
#> [25] rlang_1.1.7       fs_1.6.6          htmlwidgets_1.6.4

Zaoqu Liu

2026-01-23