---

name: cellcellcommunicationplots description: Visualize cell-cell communication inference results from CellCellCommunication process. Creates publication-ready network diagrams, heatmaps, and interaction plots to help interpret ligand-receptor interactions between cell types.

CellCellCommunicationPlots Process Configuration

Purpose

Visualize cell-cell communication inference results from CellCellCommunication process. Creates publication-ready network diagrams, heatmaps, and interaction plots to help interpret ligand-receptor interactions between cell types.

When to Use

• After running CellCellCommunication to visualize inferred interactions
• To create publication-quality figures for cell communication analysis
• For exploring signaling pathways between cell populations
• When needing multiple visualization types (network, heatmap, box plots) for comprehensive analysis
• To subset and focus on specific pathways or cell type pairs of interest

Configuration Structure

Process Enablement

[CellCellCommunicationPlots]
cache = true  # Recommended to skip replotting when only adjusting parameters

Input Specification

[CellCellCommunicationPlots.in]
# cccfile: Path to the output from CellCellCommunication process
# This is typically a text file with ligand-receptor interaction data
cccfile = ["CellCellCommunication"]

Environment Variables

Data Filtering Options

[CellCellCommunicationPlots.envs]
# subset: dplyr filter expression to subset interactions
# Examples: subset = "source == 'T cells' & target == 'Macrophages'"
#          subset = "pathway == 'TNF signaling'"
subset = ""

# magnitude: Column name for interaction magnitude/strength
# Default: second last column in the data
# Common values: "magnitude", "score", "probability", "importance"
magnitude = "importance"

# specificity: Column name for interaction specificity
# Default: last column in the data
# Set to null (None) if method doesn't have specificity metric
specificity = "specificity"

Plot Rendering Parameters

[CellCellCommunicationPlots.envs.devpars]
# Plot resolution and dimensions
res = 100       # Resolution in DPI (default: 100)
height = 2000   # Plot height in pixels (optional)
width = 2000    # Plot width in pixels (optional)

[CellCellCommunicationPlots.envs]
# Additional output formats beyond PNG
more_formats = ["pdf", "svg"]  # Default: []

# Description shown in report
descr = "Cell-cell communication plot"

Cases Configuration (Multiple Plots)

[CellCellCommunicationPlots.envs.cases]
# Dictionary of plot configurations
# Keys = plot names (used in report filenames)
# Values = arguments passed to scplotter::CCCPlot

# Default case if none specified
[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication"]
plot_type = "network"

CellChat Plot Types

Available Plot Types

network

Description: Network plot with cell types as nodes and interactions as edges. Best for overview of communication patterns.

Use when: You want to see the global communication network structure, identify major signaling hubs, or visualize cell type connectivity.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Network"]
plot_type = "network"
legend-position = "none"  # Remove legend for cleaner look
theme = "theme_blank"
theme_args = {add_coord = false}

chord / circos

Description: Circular chord diagram showing interaction flows between cell types. Equivalent to "circos" (alias).

Use when: You want to visualize bidirectional communication flows in a compact circular format, excellent for publication figures.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Circos Plot"]
plot_type = "circos"

heatmap

Description: Heatmap matrix with source cell types as rows and target cell types as columns. Color intensity represents interaction strength.

Use when: You want to identify strong/weak interactions at a glance, compare interaction patterns across many cell pairs, or find hotspots of communication.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Heatmap"]
plot_type = "heatmap"

sankey / alluvial

Description: Sankey/alluvial flow diagram showing communication as flows from senders to receivers. Equivalent to "alluvial" (alias).

Use when: You want to visualize communication as flows, highlight major pathways, or show directional emphasis.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Sankey"]
plot_type = "sankey"

dot

Description: Dot plot showing interaction strength with dot size/color. Compact alternative to network plots.

Use when: You have many cell types and network plot becomes too dense, or when you prefer a more compact visualization.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Dot"]
plot_type = "dot"
x_text_angle = 90  # Rotate x-axis labels for readability

box

Description: Box plots for source cell types, where each x is a target cell type and values are interaction strengths of ligand-receptor pairs. Requires method = "interaction".

Use when: You want to see the distribution of interaction strengths, identify outliers, or compare variability across interactions.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Interaction (Box Plot)"]
plot_type = "box"
method = "interaction"
x_text_angle = 90

violin

Description: Violin plots similar to box plots but showing full distribution shape. Requires method = "interaction".

Use when: You want to see the full distribution of interaction strengths, identify multi-modal patterns, or compare distribution shapes.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Violin"]
plot_type = "violin"
method = "interaction"
add_box = true  # Add box plot overlay

ridge

Description: Ridge (joy) plots for source cell types, each row is a target cell type. Requires method = "interaction".

Use when: You want to compare distributions across many target cell types, or when you prefer a compact density visualization.

Example:

[CellCellCommunicationPlots.envs.cases."Cell-Cell Communication Ridge"]
plot_type = "ridge"
method = "interaction"

Method Parameter

Two methods control what data is plotted:

method = "aggregation" (default)

• Aggregates ligand-receptor pairs for each source-target pair
• Shows only source/target cell type pairs
• Best for overview visualization

method = "interaction"

• Plots individual ligand-receptor pair interactions
• Shows specific ligand-receptor pairs driving communication
• Required for box, violin, and ridge plot types
• Best for detailed pathway analysis

Configuration Examples

Minimal Configuration (Default Plot)

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

Overview Plots (Quick Exploration)

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

[CellCellCommunicationPlots.envs.cases."Network Overview"]
plot_type = "network"
legend-position = "right"

[CellCellCommunicationPlots.envs.cases."Heatmap Overview"]
plot_type = "heatmap"

Pathway-Specific Plots (Deep Dive)

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

# Focus on specific pathways (uses subset filter)
[CellCellCommunicationPlots.envs.cases."TNF Signaling"]
plot_type = "network"
subset = "pathway == 'TNF signaling'"
legend-position = "none"

[CellCellCommunicationPlots.envs.cases."IL6 Pathway"]
plot_type = "heatmap"
subset = "pathway == 'IL-6 signaling'"

Cell Type Pair Focus

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

# Focus on specific sender-receiver pairs
[CellCellCommunicationPlots.envs.cases."T cell to Macrophage"]
plot_type = "box"
method = "interaction"
subset = "source == 'T cell' & target == 'Macrophage'"

[CellCellCommunicationPlots.envs.cases."T cell to B cell"]
plot_type = "dot"
subset = "source == 'T cell' & target == 'B cell'"

Publication Figure Set (Comprehensive)

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

[CellCellCommunicationPlots.envs.devpars]
res = 300  # High resolution for publication

[CellCellCommunicationPlots.envs]
more_formats = ["pdf", "svg"]

[CellCellCommunicationPlots.envs.cases."Figure 1A: Network"]
plot_type = "network"
legend-position = "none"
theme = "theme_blank"
theme_args = {add_coord = false}

[CellCellCommunicationPlots.envs.cases."Figure 1B: Heatmap"]
plot_type = "heatmap"

[CellCellCommunicationPlots.envs.cases."Figure 1C: Circos"]
plot_type = "circos"

[CellCellCommunicationPlots.envs.cases."Supplementary: Interaction Distribution"]
plot_type = "box"
method = "interaction"
x_text_angle = 90

Table Output (Data Export)

[CellCellCommunicationPlots.in]
cccfile = ["CellCellCommunication"]

[CellCellCommunicationPlots.envs.cases."Interaction Table"]
plot_type = "table"  # Special type to export data as text file

Common Patterns

Pattern 1: Overview + Detail

# First, get a global overview with network and heatmap
[CellCellCommunicationPlots.envs.cases."Global Network"]
plot_type = "network"

[CellCellCommunicationPlots.envs.cases."Global Heatmap"]
plot_type = "heatmap"

# Then, explore top pathways in detail
[CellCellCommunicationPlots.envs.cases."Top Pathway Interactions"]
plot_type = "box"
method = "interaction"
subset = "pathway == 'CXCL' | pathway == 'CCL'"

Pattern 2: Comparing Specific Cell Types

# All communications from T cells
[CellCellCommunicationPlots.envs.cases."T cell as Sender"]
plot_type = "chord"
subset = "source == 'T cell'"

# All communications to T cells
[CellCellCommunicationPlots.envs.cases."T cell as Receiver"]
plot_type = "chord"
subset = "target == 'T cell'"

# Specific interaction pair with full detail
[CellCellCommunicationPlots.envs.cases."T cell <-> Macrophage Details"]
plot_type = "violin"
method = "interaction"
subset = "(source == 'T cell' & target == 'Macrophage') | (source == 'Macrophage' & target == 'T cell')"

Pattern 3: Publication-Ready Workflow

# Set high resolution for all plots
[CellCellCommunicationPlots.envs.devpars]
res = 300
height = 2500
width = 2500

[CellCellCommunicationPlots.envs]
more_formats = ["pdf"]  # Export as PDF for publication

# Main figure: clean network
[CellCellCommunicationPlots.envs.cases."Main Figure"]
plot_type = "network"
legend-position = "none"
theme = "theme_blank"
theme_args = {add_coord = false}

# Supplementary: detailed heatmap
[CellCellCommunicationPlots.envs.cases."Supplementary Heatmap"]
plot_type = "heatmap"

# Supplementary: interaction distribution
[CellCellCommunicationPlots.envs.cases."Supplementary Distribution"]
plot_type = "ridge"
method = "interaction"

Dependencies

Upstream Processes

• CellCellCommunication: Required. Provides the ligand-receptor interaction data file (cccfile). This process must complete successfully with interaction inference.

Downstream Processes

• None: CellCellCommunicationPlots is a terminal visualization process. No downstream processes depend on its output.

Validation Rules

Input Validation

• cccfile must point to a valid output file from CellCellCommunication
• File must contain columns: source, target, ligand, receptor, plus magnitude/specificity columns

Plot Type Validation

• Valid plot_type values: "dot", "network", "chord", "circos", "heatmap", "sankey", "alluvial", "box", "violin", "ridge"
• "box", "violin", and "ridge" require method = "interaction"

Subset Expression Validation

• subset must be valid dplyr::filter() expression
• Column names in expression must exist in the input data

Method Validation

• Valid method values: "aggregation", "interaction"

Troubleshooting

Issue: Too many edges in network plot

Problem: Network plot becomes unreadable with many interactions

Solutions:

• Use subset to filter to specific pathways or cell types:

  subset = "pathway %in% c('TNF', 'CXCL', 'CCL')"
  subset = "importance > 0.5"  # Filter by strength
  

• Use heatmap instead for better overview with many cell types
• Use circos for more compact visualization

Issue: Unreadable axis labels

Problem: Cell type labels overlap or are too small

Solutions:

# Rotate x-axis labels
x_text_angle = 90

# For network plots, adjust legend position
legend-position = "bottom"

# For heatmap, toggle row/column name display
show_row_names = true
show_column_names = true

Issue: Box/violin plots show nothing

Problem: Box, violin, or ridge plots are empty

Cause: These plot types require method = "interaction"

Solution:

[CellCellCommunicationPlots.envs.cases."My Plot"]
plot_type = "box"  # or "violin", "ridge"
method = "interaction"  # Required!

Issue: Specificity column not found

Problem: Error about missing specificity column

Cause: Some inference methods don't provide specificity scores

Solution:

[CellCellCommunicationPlots.envs]
specificity = null  # Set to null if not available
# OR specify a different column:
specificity = "pvalue"

Issue: Plot file too large

Problem: High-resolution plots consume too much disk space

Solutions:

[CellCellCommunicationPlots.envs.devpars]
res = 100  # Reduce from 300 to 100 DPI

[CellCellCommunicationPlots.envs]
more_formats = []  # Remove PDF/SVG exports, keep only PNG

Issue: Incorrect magnitude/specificity columns

Problem: Plots show unexpected values or are blank

Cause: Default column selection (second last / last) doesn't match your data

Solution: Explicitly specify correct column names

[CellCellCommunicationPlots.envs]
# Check your cccfile headers and match these:
magnitude = "score"  # or "probability", "importance", etc.
specificity = "pvalue"  # or "specificity", "fdr", etc.

External References

• scplotter::CCCPlot: https://pwwang.github.io/scplotter/reference/CCCPlot.html
• LIANA methods: https://liana-py.readthedocs.io/en/latest/notebooks/basic_usage.html#Tileplot
• CellChat: http://www.cellchat.org/
• CCPlotR: https://bioconductor.org/packages/CCPlotR/
• Cell-cell communication best practices: https://www.sc-best-practices.org/mechanisms/cell_cell_communication.html