Visualization
Single-cell and omics visualization agent
lobster-visualization
FreeBeginner
Publication-quality visualizations: UMAP, violin plots, heatmaps, and volcano plots
Input
AnnDataDE ResultsCluster LabelsGene Lists
Output
UMAP PlotsViolin PlotsHeatmapsVolcano PlotsDot Plots
Agents (1)
└── visualization_expert — Publication-quality plotting and figure generation
pip install lobster-visualization
Agents
visualization_expert
Specialized agent for creating visualizations from analyzed omics data.
Capabilities:
- UMAP/t-SNE plots with gene overlays
- Violin plots and dot plots
- Heatmaps with clustering
- Volcano plots and MA plots
- Custom plot styling
Supported Plot Types
| Plot Type | Use Case | Best For |
|---|---|---|
| UMAP | Dimensionality reduction | Cell clustering, embeddings |
| t-SNE | Dimensionality reduction | Small datasets, local structure |
| Violin | Gene expression distribution | Comparing expression across groups |
| Dot plot | Gene markers across clusters | Marker gene visualization |
| Heatmap | Expression patterns | Pathway/gene set analysis |
| Volcano | Differential expression | Identifying significant genes |
| MA plot | Fold change vs abundance | Quality check for DE analysis |
Services
lobster-visualization includes visualization services bundled with the package:
| Service | Purpose |
|---|---|
| SingleCellVisualizationService | UMAP, t-SNE, violin plots, dot plots, heatmaps |
The service is installed automatically with the agent package.
Example Workflows
UMAP with Gene Overlay
User: Create a UMAP colored by cell type with CD3E expression overlay
[visualization_expert]
- Generates UMAP with categorical coloring
- Overlays CD3E expression as continuous color
- Saves to exports/
- Returns plot pathMarker Gene Dot Plot
User: Show me the expression of marker genes across clusters
[visualization_expert]
- Retrieves marker genes from analysis state
- Creates dot plot with cluster on x-axis
- Size = percent expressing, color = mean expression
- Saves publication-ready figureVolcano Plot for DE Results
User: Make a volcano plot from my differential expression results
[visualization_expert]
- Loads DE results from transcriptomics analysis
- Plots log2 fold change vs -log10 p-value
- Highlights significant genes (|FC| > 1, padj < 0.05)
- Labels top significant genesDependencies
lobster-visualization requires visualization libraries:
| Library | Purpose |
|---|---|
| matplotlib | Core plotting library |
| seaborn | Statistical visualizations |
| scanpy | Single-cell plotting integration |
| plotly | Interactive plots |
These are installed automatically with the package.
Configuration
# .lobster_workspace/config.toml
enabled = ["visualization_expert"]Plot Storage
Generated plots are saved to workspace exports:
.lobster_workspace/exports/
|-- umap_cell_type.png
|-- umap_CD3E_overlay.png
|-- violin_marker_genes.png
|-- dotplot_clusters.png
|-- heatmap_top_genes.png
|-- volcano_treated_vs_control.pngPlot Customization
visualization_expert supports plot customization through natural language:
User: Make the UMAP larger with a white background and save as PDF
[visualization_expert adjusts]
- Figure size: 12x10 inches
- Background: white
- Format: PDF vector graphicsCustomizable Parameters
| Parameter | Options | Default |
|---|---|---|
| Figure size | Any dimensions | 8x6 inches |
| DPI | 72-600 | 150 |
| Format | PNG, PDF, SVG | PNG |
| Color palette | Any matplotlib/seaborn | Default |
| Font size | Any size | 12pt |
| Background | White, transparent | White |
Integration with Analysis Agents
visualization_expert receives data from analysis agents:
transcriptomics_expert
-> Performs clustering
-> Stores results in state
-> Calls visualization_expert
-> Reads cluster assignments
-> Generates UMAP plot