Visualization Gallery

Overview

Connectome provides a comprehensive suite of visualization functions for exploring cell-cell communication networks. This gallery demonstrates each visualization type with example outputs and interpretation guidelines.

Example Data Preparation

All visualizations in this gallery use a mock connectome dataset for demonstration:

library(Connectome)

# Create mock connectome for demonstration
set.seed(42)
cell_types <- c("Epithelial", "Fibroblast", "Macrophage", "T_cell", "Endothelial")
ligands <- c("VEGFA", "IL6", "TNF", "CXCL12", "FGF2", "TGFB1")
receptors <- c("KDR", "IL6R", "TNFRSF1A", "CXCR4", "FGFR1", "TGFBR1")
modes <- c("growth_factor", "cytokine", "cytokine", "chemokine", "growth_factor", "growth_factor")

n_edges <- 150
mock_connectome <- data.frame(
  source = sample(cell_types, n_edges, replace = TRUE),
  target = sample(cell_types, n_edges, replace = TRUE),
  ligand = sample(ligands, n_edges, replace = TRUE),
  receptor = sample(receptors, n_edges, replace = TRUE),
  mode = sample(modes, n_edges, replace = TRUE),
  ligand.expression = runif(n_edges, 0.5, 3),
  recept.expression = runif(n_edges, 0.5, 3),
  ligand.scale = rnorm(n_edges, 0, 1),
  recept.scale = rnorm(n_edges, 0, 1),
  percent.source = runif(n_edges, 0.1, 0.8),
  percent.target = runif(n_edges, 0.1, 0.8),
  weight_norm = runif(n_edges, 0.5, 5),
  weight_sc = runif(n_edges, -2, 2)
)
mock_connectome$pair <- paste(mock_connectome$ligand, mock_connectome$receptor, sep = "_")
mock_connectome$vector <- paste(mock_connectome$source, mock_connectome$target, sep = " - ")

# Filter for visualization
mock_filtered <- mock_connectome[mock_connectome$weight_norm > 2, ]

Network Visualizations

1. NetworkPlot

The NetworkPlot() function creates an igraph-based directed network visualization where nodes represent cell populations and edges represent signaling interactions.

NetworkPlot(
  connectome_filtered,
  weight.attribute = "weight_norm",
  title = "Cell-Cell Communication Network"
)

Network plot showing cell-cell communication. Nodes represent cell types, edges represent signaling interactions, and edge width is proportional to interaction strength.

Interpretation:

Nodes: Each node represents a cell population
Edges: Directed arrows indicate signaling direction (sender → receiver)
Edge width: Proportional to edge weight (interaction strength)
Edge color: Gradient from light to dark based on weight
Layout: Force-directed layout positions strongly connected nodes closer together

2. CircosPlot

Chord diagrams provide an elegant circular representation of connectivity patterns, ideal for visualizing complex multi-cellular communication networks.

CircosPlot(
  connectome_filtered,
  weight.attribute = "weight_norm",
  lab.cex = 0.8
)

Circos plot showing ligand-receptor interactions. Sectors represent ligands (outer) and receptors (inner), with ribbons connecting interacting pairs.

Interpretation:

Sectors: Represent ligands and receptors grouped by signaling mode
Ribbons: Connect ligand-receptor pairs with active interactions
Ribbon width: Proportional to interaction strength
Colors: Indicate source cell type or signaling mode

Dot Plot Visualizations

3. EdgeDotPlot

Matrix visualization showing all edges meeting specified criteria, perfect for systematic comparison across cell-cell vectors.

EdgeDotPlot(connectome_filtered)

Edge dot plot showing ligand-receptor pairs (y-axis) across cell-cell communication vectors (x-axis). Dot size represents normalized weight, color represents scaled weight.

Reading the plot:

X-axis: Cell-cell vectors (Source → Target)
Y-axis: Ligand-Receptor pairs
Dot size: log(weight_norm + 1) - larger dots indicate stronger interactions
Dot color: log(weight_sc + 1) - color intensity reflects z-scored expression

Centrality Analysis

4. Centrality

Hub and authority score analysis stratified by signaling mode, revealing which cell types are central senders or receivers in specific pathways.

Centrality(
  connectome_filtered,
  group.by = "mode",
  weight.attribute = "weight_sc"
)

Centrality analysis showing hub scores (sending importance, left) and authority scores (receiving importance, right) for each cell type across signaling modes.

Interpretation:

Hub score: Measures importance as a signal sender
Authority score: Measures importance as a signal receiver
Higher scores: Indicate more central role in the signaling network
Mode stratification: Allows comparison across different signaling families

Scatter Plots

5. SignalScatter

Explore specific signaling mechanisms by visualizing ligand-receptor expression patterns across cell-cell vectors.

SignalScatter(
  connectome_filtered,
  features = c("VEGFA", "IL6"),
  weight.attribute = "weight_norm",
  label.threshold = 1.5
)

Signal scatter plot showing expression patterns for selected ligands. Each point represents a cell-cell interaction vector, with position determined by ligand/receptor expression.

Plot elements:

X-axis: Receptor expression (scaled)
Y-axis: Ligand expression (scaled)
Dot size: Edge weight
Color: Different ligand-receptor pairs
Labels: Cell-cell vectors above threshold

Customization Tips

Color Palettes

# Custom cell type colors
my_colors <- c(
  "Epithelial" = "#E41A1C",
  "Fibroblast" = "#377EB8", 
  "Macrophage" = "#4DAF4A",
  "T_cell" = "#984EA3",
  "Endothelial" = "#FF7F00"
)

NetworkPlot(connectome_filtered, cols.use = my_colors)
CircosPlot(connectome_filtered, cols.use = my_colors)

Filtering Before Plotting

# Filter to specific interactions
conn_subset <- FilterConnectome(
  connectome,
  modes.include = c("growth_factor", "cytokine"),
  sources.include = c("Fibroblast", "Macrophage"),
  min.pct = 0.1,
  min.z = 0
)

NetworkPlot(conn_subset)

Saving High-Quality Figures

# PDF for publication
pdf("network_plot.pdf", width = 10, height = 8)
NetworkPlot(connectome_filtered)
dev.off()

# PNG for presentations
png("circos_plot.png", width = 1200, height = 1000, res = 150)
CircosPlot(connectome_filtered)
dev.off()

Visualization Selection Guide

Question	Recommended Plot
Overall network topology	`NetworkPlot()`
Circular connectivity view	`CircosPlot()`
Edge details matrix	`EdgeDotPlot()`
Centrality by signaling mode	`Centrality()`
Specific gene interactions	`SignalScatter()`
Specific cell pair	`CellCellScatter()`
Compare conditions	`CompareCentrality()`, `CircosDiff()`
Differential analysis	`DiffEdgeDotPlot()`, `DifferentialScoringPlot()`

Session Info

sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] rmarkdown_2.31
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39    R6_2.6.1         fastmap_1.2.0    xfun_0.59       
#>  [5] maketools_1.3.2  cachem_1.1.0     knitr_1.51       htmltools_0.5.9 
#>  [9] buildtools_1.0.0 lifecycle_1.0.5  cli_3.6.6        sass_0.4.10     
#> [13] jquerylib_0.1.4  compiler_4.6.0   sys_3.4.3        tools_4.6.0     
#> [17] evaluate_1.0.5   bslib_0.11.0     yaml_2.3.12      otel_0.2.0      
#> [21] jsonlite_2.0.0   rlang_1.2.0