cancer-research-figure-guide

Cancer Research (AACR) Figure Preparation Guide

Overview

This guide provides the complete specifications for preparing figures for submission to Cancer Research and other AACR (American Association for Cancer Research) journals. Cancer Research has a distinctive hierarchical panel labeling system (Ai, Aii, Bi, Bii) and strict limits on the total number of display items.

Official reference: https://aacrjournals.org/pages/article-style-and-format

Resolution Requirements

| Image Type | Minimum Resolution | |---|---| | Line art | 1,200 DPI | | Halftone / color images | 300 DPI | | Combination artwork | 600-900 DPI |

from PIL import Image

def check_cancer_res_resolution(image_path, image_type='halftone'):
    """Check if image meets Cancer Research resolution requirements.

    Args:
        image_type: 'lineart' (1200), 'halftone' (300), or 'combination' (600)
    """
    min_dpi = {'lineart': 1200, 'halftone': 300, 'combination': 600}
    required = min_dpi.get(image_type, 300)

    img = Image.open(image_path)
    dpi = img.info.get('dpi', (72, 72))

    print(f"Type: {image_type} | Required: {required} DPI | Actual: {dpi[0]} DPI")
    passed = dpi[0] >= required
    print("PASS" if passed else f"FAIL: Need {required} DPI minimum")

    return passed

File Format

| Format | Accepted | |---|---| | EPS | Yes | | TIFF | Yes | | AI (Adobe Illustrator) | Yes | | PSD (Photoshop) | Yes | | PNG | Yes | | PS (PostScript) | Yes |

Figure Size and Dimensions

Display Item Limits

| Article Type | Maximum Display Items | |---|---| | Research Articles | 7 figures + tables combined | | Letters | 2 display items total |

• Each figure must fit on a single printed page
• Figures should be presented in sequential order adjacent to legends

Color Mode

• No strict CMYK/RGB mandate in published guidelines
• RGB recommended for online display
• Follow general best practices for color accessibility

Font Requirements

| Element | Font | Size | |---|---|---| | Manuscript body text | Arial, Helvetica, or Times New Roman | 12 pt | | Figure text | Same fonts | 8-12 pt range |

Labeling Conventions

Hierarchical Panel Label System (AACR-Specific)

Cancer Research uses a unique three-level hierarchical labeling system:

1. Level 1: Capital letters — A, B, C, D
2. Level 2: Roman numerals — i, ii, iii, iv
3. Level 3: Lowercase letters — a, b, c, d

Preferred format: Ai, Aii, Bi, Bii (NOT Aa, Ab, Ba, Bb)

Labeling Rules

• No boxes around panel labels
• No periods after panel labels
• Multiple panels should each be labeled and described in the legend

Legend Requirements

• Figure legends must include the number of technical AND biological replicates
• This is a mandatory requirement for Cancer Research

def generate_cancer_res_labels(n_main_panels, sub_panels_per_main=None):
    """Generate Cancer Research hierarchical panel labels.

    Args:
        n_main_panels: Number of main panels (A, B, C, ...)
        sub_panels_per_main: List of sub-panel counts per main panel,
                             or None for no sub-panels

    Returns:
        List of label strings

    Example:
        generate_cancer_res_labels(3, [2, 3, 1])
        # Returns: ['Ai', 'Aii', 'Bi', 'Bii', 'Biii', 'C']
    """
    import string

    labels = []
    roman = ['i', 'ii', 'iii', 'iv', 'v', 'vi', 'vii', 'viii']

    for i in range(n_main_panels):
        main_label = string.ascii_uppercase[i]

        if sub_panels_per_main and sub_panels_per_main[i] > 1:
            for j in range(sub_panels_per_main[i]):
                labels.append(f"{main_label}{roman[j]}")
        else:
            labels.append(main_label)

    return labels

Example Usage

# 3 main panels: A has 2 sub-panels, B has 3, C has 1
labels = generate_cancer_res_labels(3, [2, 3, 1])
print(labels)
# Output: ['Ai', 'Aii', 'Bi', 'Bii', 'Biii', 'C']

Image Integrity and Manipulation Policy

Cancer Research follows general AACR editorial policies for image integrity:

• All image adjustments should be applied uniformly to the entire image
• No selective enhancement of specific features
• Original unprocessed data should be available upon request
• Processing details should be described in Methods

Python Quick Start: Full Validation

from PIL import Image
import os

def validate_cancer_res_figure(image_path, image_type='halftone'):
    """Full validation of a figure against Cancer Research requirements."""
    img = Image.open(image_path)
    issues = []

    # 1. Resolution check
    min_dpi = {'lineart': 1200, 'halftone': 300, 'combination': 600}
    required = min_dpi.get(image_type, 300)
    dpi = img.info.get('dpi', (72, 72))
    if dpi[0] < required:
        issues.append(f"Resolution {dpi[0]} DPI below {required} DPI for {image_type}")

    # 2. Color mode check
    if img.mode not in ('RGB', 'RGBA'):
        issues.append(f"Color mode is {img.mode}; RGB recommended")

    # 3. Format check
    fmt = img.format
    accepted = ['TIFF', 'EPS', 'PNG', 'JPEG', 'PDF']
    if fmt and fmt.upper() not in accepted:
        issues.append(f"Format '{fmt}' not in standard list")

    # Report
    print(f"=== Cancer Research Figure Validation ===")
    print(f"Dimensions: {img.size[0]} x {img.size[1]} px")
    print(f"DPI: {dpi[0]} x {dpi[1]}")
    print(f"Color mode: {img.mode}")
    print(f"Format: {fmt}")

    if issues:
        print(f"\nISSUES FOUND ({len(issues)}):")
        for issue in issues:
            print(f"  - {issue}")
    else:
        print("\nAll checks PASSED")

    print("\nREMINDER: Cancer Research limits Research Articles to 7 figures+tables total")
    print("REMINDER: Legends must include number of technical AND biological replicates")

    return len(issues) == 0

Key Concepts

Hierarchical Panel Labeling

Cancer Research uses a unique three-level labeling system: Level 1 uses capital letters (A, B, C), Level 2 uses Roman numerals (i, ii, iii), and Level 3 uses lowercase letters (a, b, c). The preferred format is Ai, Aii, Bi, Bii — not Aa, Ab, Ba, Bb. No boxes or periods surround panel labels.

Display Item Limits

Cancer Research strictly limits the total number of display items (figures + tables combined). Research Articles are limited to 7 display items; Letters are limited to 2. Planning figure composition to maximize information density within these limits is essential.

Replicate Reporting in Legends

A distinctive Cancer Research requirement is that figure legends must include the number of both technical and biological replicates for each experiment shown. This transparency standard supports reproducibility and is checked during editorial review.

Decision Framework

What type of image are you preparing?
├── Line art (diagram, schematic) → 1,200 DPI
├── Halftone (photo, micrograph) → 300 DPI
├── Combination (mixed text + image) → 600-900 DPI
└── Multi-panel figure
    ├── Simple panels (A, B, C) → Standard uppercase labels
    └── Sub-panels needed → Hierarchical: Ai, Aii, Bi, Bii

| Scenario | Format | Resolution | Labeling | |---|---|---|---| | Western blot with quantification | TIFF + EPS | 300 DPI (blot) + 1,200 DPI (graph) | Ai (blot), Aii (quantification) | | Tumor growth curves | EPS or AI | Vector or 1,200 DPI | A, B, C (simple panels) | | Immunohistochemistry panel | TIFF | 300 DPI | Ai, Aii, Bi, Bii (conditions x magnifications) | | Flow cytometry dot plots | TIFF or PDF | 300 DPI | Hierarchical by condition | | Kaplan-Meier survival curves | EPS or PDF | Vector | Simple A, B labeling |

Best Practices

1. Plan display items early: With a 7-item limit for Research Articles, plan which results become figures vs. tables vs. supplementary material before drafting
2. **Use hierarchical labels for complex