Figure Pipeline

Five-Step Figure Generation Pipeline

Generate publication-quality scientific figures through a structured pipeline with reference retrieval, planning, styling, generation, and self-critique. Inspired by PaperVizAgent's five-agent architecture.

When to Use

• Multi-panel composite figures (e.g., "Figure 1: A) boxplot, B) KM curve, C) heatmap, D) network")
• Figures that need to match a specific journal's style
• Complex visualizations where direct code generation often produces suboptimal layouts
• User explicitly asks for "publication-quality" or "journal-ready" figures
• Graphical abstracts

When NOT to use (overkill):

• Single simple plot → just write matplotlib/ggplot2 code directly
• Mechanism diagrams → use svg-scientific-figures skill
• Quick exploratory plots → direct code

Pipeline Steps

Step 1: RETRIEVE — Find Reference Figures

Search for published figures on the same topic to establish visual expectations:

bash: echo "=== PubMed figure captions ===" && \
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=10&sort=relevance&term=TOPIC+AND+(figure+OR+visualization)" && \
echo -e "\n=== bioRxiv recent figures ===" && \
curl -s "https://api.biorxiv.org/details/biorxiv/2025-01-01/2026-03-18?jou=biorxiv" 2>/dev/null | head -c 2000

Also search for figure captions in full-text papers via Asta or Europe PMC:

curl -s "https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=TOPIC+AND+FIG_TYPE:figure&format=json&pageSize=5"

From reference figures, extract:

• Figure types used (violin plot vs boxplot, clustered vs simple heatmap)
• Panel arrangement (2x2 grid, horizontal strip, vertical stack)
• Color schemes (which journal palette)
• Annotation patterns (significance brackets, gene labels, axis formatting)

Step 2: PLAN — Design Figure Composition

Create a structured figure plan:

{
  "figure_id": "Figure 1",
  "title": "THBS2 Expression and Survival Analysis Across Cancer Types",
  "layout": {
    "type": "grid",
    "rows": 2,
    "cols": 2,
    "width_cm": 17.5,
    "height_cm": 15
  },
  "panels": [
    {
      "id": "A",
      "type": "boxplot",
      "data": "TCGA pan-cancer expression",
      "x": "cancer_type",
      "y": "THBS2_expression_TPM",
      "notes": "Sort by median expression, highlight significant (red asterisks)"
    },
    {
      "id": "B",
      "type": "kaplan_meier",
      "data": "PAAD survival",
      "groups": "THBS2_high vs THBS2_low",
      "notes": "Include risk table, log-rank p, HR with 95%CI"
    },
    {
      "id": "C",
      "type": "heatmap",
      "data": "Immune cell correlation matrix",
      "notes": "Cluster by correlation, annotate r values for significant pairs"
    },
    {
      "id": "D",
      "type": "dot_plot",
      "data": "GO enrichment top 15 terms",
      "notes": "Color by p-value, size by gene count, order by enrichment score"
    }
  ],
  "shared_style": {
    "palette": "NPG",
    "font": "Arial",
    "label_size": 8,
    "title_size": 10
  }
}

Step 3: STYLE — Apply Journal-Specific Formatting

Select and apply styling rules based on target journal:

Styling rules:

• Axis labels: 8-9pt, sentence case
• Axis tick labels: 7-8pt
• Panel labels: 10-12pt, bold, top-left corner outside plot area
• Legend: 7-8pt, outside plot or in open space
• Significance annotations: * p<0.05, ** p<0.01, *** p<0.001, ns not significant
• Color: use colorblind-friendly palettes; never use red-green only
• Grid lines: minimal or none; let data speak

Step 4: VISUALIZE — Generate the Figure

Write Python or R code implementing the planned figure. Use the styling rules from Step 3.

Python approach (for multi-panel):

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(17.5/2.54, 15/2.54), dpi=300)
gs = gridspec.GridSpec(2, 2, hspace=0.35, wspace=0.35)

ax_a = fig.add_subplot(gs[0, 0])
# Panel A code...
ax_a.text(-0.15, 1.05, 'A', transform=ax_a.transAxes, fontsize=12, fontweight='bold')

ax_b = fig.add_subplot(gs[0, 1])
# Panel B code...

# ... etc

plt.savefig(out_path, dpi=300, bbox_inches='tight', facecolor='white')

R approach (using patchwork):

library(ggplot2); library(patchwork)
p_a <- ggplot(...) + labs(tag = "A")
p_b <- ggplot(...) + labs(tag = "B")
combined <- (p_a | p_b) / (p_c | p_d)
ggsave(out_path, combined, width=17.5, height=15, units="cm", dpi=300)

Step 5: CRITIQUE — Review and Refine

After generating the figure, perform a quality review:

Checklist:

1. All panel labels (A, B, C, D) present and correctly positioned?
2. All axis labels present, readable, and correctly spelled?
3. Legend present where needed? Not obscuring data?
4. Font sizes consistent across all panels?
5. Color palette consistent across all panels?
6. Significance annotations correct and complete?
7. White space balanced? No cramped or empty areas?
8. Data accurately represented? (spot-check values against source data)
9. Figure would be understandable from caption alone?
10. Resolution sufficient for print (300 DPI at target size)?

If any check fails, go back to Step 4 and fix. Maximum 2 refinement rounds.

After passing critique:

✅ Figure review passed (10/10 checks)
Saved: figures/figure1_thbs2_landscape.png (17.5 x 15 cm, 300 DPI)

Output

Integration with Research Recipes

When a Recipe generates multiple figure outputs, offer to combine them into a composite figure using this pipeline:

"已生成 4 张独立图表。需要用 Figure Pipeline 组装为一张多面板组合图吗？"