Add Seaborn integration for enhanced statistical visualization techniques in scientific plots. Include quick start guides, common plot types, and best practices for publication-ready figures, emphasizing colorblind-safe palettes and automatic confidence intervals.

2026-01-26 16:58:56 +08:00 · 2025-10-21 00:16:38 -07:00
parent 02dae0f30c
commit 6e8d1c29b9
1 changed files with 335 additions and 4 deletions
--- a/scientific-thinking/scientific-visualization/SKILL.md
+++ b/scientific-thinking/scientific-visualization/SKILL.md
@@ -80,6 +80,35 @@ fig, ax = plt.subplots()
 # ... your plotting code ...
 ```

+### Quick Start with Seaborn
+
+For statistical plots, use seaborn with publication styling:
+
+```python
+import seaborn as sns
+import matplotlib.pyplot as plt
+from style_presets import apply_publication_style
+
+# Apply publication style
+apply_publication_style('default')
+sns.set_theme(style='ticks', context='paper', font_scale=1.1)
+sns.set_palette('colorblind')
+
+# Create statistical comparison figure
+fig, ax = plt.subplots(figsize=(3.5, 3))
+sns.boxplot(data=df, x='treatment', y='response', 
+            order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
+sns.stripplot(data=df, x='treatment', y='response',
+              order=['Control', 'Low', 'High'], 
+              color='black', alpha=0.3, size=3, ax=ax)
+ax.set_ylabel('Response (μM)')
+sns.despine()
+
+# Save figure
+from figure_export import save_publication_figure
+save_publication_figure(fig, 'treatment_comparison', formats=['pdf', 'png'], dpi=300)
+```
+
 ## Core Principles and Best Practices

 ### 1. Resolution and File Format
@@ -204,6 +233,18 @@ See `references/matplotlib_examples.md` Example 1 for complete code.
 6. Remove unnecessary spines
 7. Save in vector format

+**Using seaborn for automatic confidence intervals:**
+```python
+import seaborn as sns
+fig, ax = plt.subplots(figsize=(5, 3))
+sns.lineplot(data=timeseries, x='time', y='measurement',
+             hue='treatment', errorbar=('ci', 95), 
+             markers=True, ax=ax)
+ax.set_xlabel('Time (hours)')
+ax.set_ylabel('Measurement (AU)')
+sns.despine()
+```
+
 ### Task 2: Create a Multi-Panel Figure

 See `references/matplotlib_examples.md` Example 2 for complete code.
@@ -226,6 +267,17 @@ See `references/matplotlib_examples.md` Example 4 for complete code.
 4. Set appropriate center value for diverging maps
 5. Test appearance in grayscale

+**Using seaborn for correlation matrices:**
+```python
+import seaborn as sns
+fig, ax = plt.subplots(figsize=(5, 4))
+corr = df.corr()
+mask = np.triu(np.ones_like(corr, dtype=bool))
+sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
+            cmap='RdBu_r', center=0, square=True,
+            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)
+```
+
 ### Task 4: Prepare Figure for Specific Journal

 **Workflow:**
@@ -306,17 +358,296 @@ ax.text(1.5, max_y * 1.1, '***', ha='center', fontsize=8)
 - See `references/matplotlib_examples.md` for extensive examples

 ### Seaborn
- Built on matplotlib, inherits all matplotlib customizations
- Good for statistical plots
- Apply matplotlib styles first, then use seaborn
+
+Seaborn provides a high-level, dataset-oriented interface for statistical graphics, built on matplotlib. It excels at creating publication-quality statistical visualizations with minimal code while maintaining full compatibility with matplotlib customization.
+
+**Key advantages for scientific visualization:**
+- Automatic statistical estimation and confidence intervals
+- Built-in support for multi-panel figures (faceting)
+- Colorblind-friendly palettes by default
+- Dataset-oriented API using pandas DataFrames
+- Semantic mapping of variables to visual properties
+
+#### Quick Start with Publication Style
+
+Always apply matplotlib publication styles first, then configure seaborn:
+
 ```python
 import seaborn as sns
+import matplotlib.pyplot as plt
 from style_presets import apply_publication_style

+# Apply publication style
 apply_publication_style('default')
-sns.set_palette(['#E69F00', '#56B4E9', '#009E73'])  # Okabe-Ito colors
+
+# Configure seaborn for publication
+sns.set_theme(style='ticks', context='paper', font_scale=1.1)
+sns.set_palette('colorblind')  # Use colorblind-safe palette
+
+# Create figure
+fig, ax = plt.subplots(figsize=(3.5, 2.5))
+sns.scatterplot(data=df, x='time', y='response', 
+                hue='treatment', style='condition', ax=ax)
+sns.despine()  # Remove top and right spines
 ```

+#### Common Plot Types for Publications
+
+**Statistical comparisons:**
+```python
+# Box plot with individual points for transparency
+fig, ax = plt.subplots(figsize=(3.5, 3))
+sns.boxplot(data=df, x='treatment', y='response', 
+            order=['Control', 'Low', 'High'], palette='Set2', ax=ax)
+sns.stripplot(data=df, x='treatment', y='response',
+              order=['Control', 'Low', 'High'], 
+              color='black', alpha=0.3, size=3, ax=ax)
+ax.set_ylabel('Response (μM)')
+sns.despine()
+```
+
+**Distribution analysis:**
+```python
+# Violin plot with split comparison
+fig, ax = plt.subplots(figsize=(4, 3))
+sns.violinplot(data=df, x='timepoint', y='expression',
+               hue='treatment', split=True, inner='quartile', ax=ax)
+ax.set_ylabel('Gene Expression (AU)')
+sns.despine()
+```
+
+**Correlation matrices:**
+```python
+# Heatmap with proper colormap and annotations
+fig, ax = plt.subplots(figsize=(5, 4))
+corr = df.corr()
+mask = np.triu(np.ones_like(corr, dtype=bool))  # Show only lower triangle
+sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
+            cmap='RdBu_r', center=0, square=True,
+            linewidths=1, cbar_kws={'shrink': 0.8}, ax=ax)
+plt.tight_layout()
+```
+
+**Time series with confidence bands:**
+```python
+# Line plot with automatic CI calculation
+fig, ax = plt.subplots(figsize=(5, 3))
+sns.lineplot(data=timeseries, x='time', y='measurement',
+             hue='treatment', style='replicate',
+             errorbar=('ci', 95), markers=True, dashes=False, ax=ax)
+ax.set_xlabel('Time (hours)')
+ax.set_ylabel('Measurement (AU)')
+sns.despine()
+```
+
+#### Multi-Panel Figures with Seaborn
+
+**Using FacetGrid for automatic faceting:**
+```python
+# Create faceted plot
+g = sns.relplot(data=df, x='dose', y='response',
+                hue='treatment', col='cell_line', row='timepoint',
+                kind='line', height=2.5, aspect=1.2,
+                errorbar=('ci', 95), markers=True)
+g.set_axis_labels('Dose (μM)', 'Response (AU)')
+g.set_titles('{row_name} | {col_name}')
+sns.despine()
+
+# Save with correct DPI
+from figure_export import save_publication_figure
+save_publication_figure(g.figure, 'figure_facets', 
+                       formats=['pdf', 'png'], dpi=300)
+```
+
+**Combining seaborn with matplotlib subplots:**
+```python
+# Create custom multi-panel layout
+fig, axes = plt.subplots(2, 2, figsize=(7, 6))
+
+# Panel A: Scatter with regression
+sns.regplot(data=df, x='predictor', y='response', ax=axes[0, 0])
+axes[0, 0].text(-0.15, 1.05, 'A', transform=axes[0, 0].transAxes,
+                fontsize=10, fontweight='bold')
+
+# Panel B: Distribution comparison
+sns.violinplot(data=df, x='group', y='value', ax=axes[0, 1])
+axes[0, 1].text(-0.15, 1.05, 'B', transform=axes[0, 1].transAxes,
+                fontsize=10, fontweight='bold')
+
+# Panel C: Heatmap
+sns.heatmap(correlation_data, cmap='viridis', ax=axes[1, 0])
+axes[1, 0].text(-0.15, 1.05, 'C', transform=axes[1, 0].transAxes,
+                fontsize=10, fontweight='bold')
+
+# Panel D: Time series
+sns.lineplot(data=timeseries, x='time', y='signal', 
+             hue='condition', ax=axes[1, 1])
+axes[1, 1].text(-0.15, 1.05, 'D', transform=axes[1, 1].transAxes,
+                fontsize=10, fontweight='bold')
+
+plt.tight_layout()
+sns.despine()
+```
+
+#### Color Palettes for Publications
+
+Seaborn includes several colorblind-safe palettes:
+
+```python
+# Use built-in colorblind palette (recommended)
+sns.set_palette('colorblind')
+
+# Or specify custom colorblind-safe colors (Okabe-Ito)
+okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
+             '#0072B2', '#D55E00', '#CC79A7', '#000000']
+sns.set_palette(okabe_ito)
+
+# For heatmaps and continuous data
+sns.heatmap(data, cmap='viridis')  # Perceptually uniform
+sns.heatmap(corr, cmap='RdBu_r', center=0)  # Diverging, centered
+```
+
+#### Choosing Between Axes-Level and Figure-Level Functions
+
+**Axes-level functions** (e.g., `scatterplot`, `boxplot`, `heatmap`):
+- Use when building custom multi-panel layouts
+- Accept `ax=` parameter for precise placement
+- Better integration with matplotlib subplots
+- More control over figure composition
+
+```python
+fig, ax = plt.subplots(figsize=(3.5, 2.5))
+sns.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)
+```
+
+**Figure-level functions** (e.g., `relplot`, `catplot`, `displot`):
+- Use for automatic faceting by categorical variables
+- Create complete figures with consistent styling
+- Great for exploratory analysis
+- Use `height` and `aspect` for sizing
+
+```python
+g = sns.relplot(data=df, x='x', y='y', col='category', kind='scatter')
+```
+
+#### Statistical Rigor with Seaborn
+
+Seaborn automatically computes and displays uncertainty:
+
+```python
+# Line plot: shows mean ± 95% CI by default
+sns.lineplot(data=df, x='time', y='value', hue='treatment',
+             errorbar=('ci', 95))  # Can change to 'sd', 'se', etc.
+
+# Bar plot: shows mean with bootstrapped CI
+sns.barplot(data=df, x='treatment', y='response',
+            errorbar=('ci', 95), capsize=0.1)
+
+# Always specify error type in figure caption:
+# "Error bars represent 95% confidence intervals"
+```
+
+#### Best Practices for Publication-Ready Seaborn Figures
+
+1. **Always set publication theme first:**
+   ```python
+   sns.set_theme(style='ticks', context='paper', font_scale=1.1)
+   ```
+
+2. **Use colorblind-safe palettes:**
+   ```python
+   sns.set_palette('colorblind')
+   ```
+
+3. **Remove unnecessary elements:**
+   ```python
+   sns.despine()  # Remove top and right spines
+   ```
+
+4. **Control figure size appropriately:**
+   ```python
+   # Axes-level: use matplotlib figsize
+   fig, ax = plt.subplots(figsize=(3.5, 2.5))
+   
+   # Figure-level: use height and aspect
+   g = sns.relplot(..., height=3, aspect=1.2)
+   ```
+
+5. **Show individual data points when possible:**
+   ```python
+   sns.boxplot(...)  # Summary statistics
+   sns.stripplot(..., alpha=0.3)  # Individual points
+   ```
+
+6. **Include proper labels with units:**
+   ```python
+   ax.set_xlabel('Time (hours)')
+   ax.set_ylabel('Expression (AU)')
+   ```
+
+7. **Export at correct resolution:**
+   ```python
+   from figure_export import save_publication_figure
+   save_publication_figure(fig, 'figure_name', 
+                          formats=['pdf', 'png'], dpi=300)
+   ```
+
+#### Advanced Seaborn Techniques
+
+**Pairwise relationships for exploratory analysis:**
+```python
+# Quick overview of all relationships
+g = sns.pairplot(data=df, hue='condition', 
+                 vars=['gene1', 'gene2', 'gene3'],
+                 corner=True, diag_kind='kde', height=2)
+```
+
+**Hierarchical clustering heatmap:**
+```python
+# Cluster samples and features
+g = sns.clustermap(expression_data, method='ward', 
+                   metric='euclidean', z_score=0,
+                   cmap='RdBu_r', center=0, 
+                   figsize=(10, 8), 
+                   row_colors=condition_colors,
+                   cbar_kws={'label': 'Z-score'})
+```
+
+**Joint distributions with marginals:**
+```python
+# Bivariate distribution with context
+g = sns.jointplot(data=df, x='gene1', y='gene2',
+                  hue='treatment', kind='scatter',
+                  height=6, ratio=4, marginal_kws={'kde': True})
+```
+
+#### Common Seaborn Issues and Solutions
+
+**Issue: Legend outside plot area**
+```python
+g = sns.relplot(...)
+g._legend.set_bbox_to_anchor((0.9, 0.5))
+```
+
+**Issue: Overlapping labels**
+```python
+plt.xticks(rotation=45, ha='right')
+plt.tight_layout()
+```
+
+**Issue: Text too small at final size**
+```python
+sns.set_context('paper', font_scale=1.2)  # Increase if needed
+```
+
+#### Additional Resources
+
+For more detailed seaborn information, see:
+- `scientific-packages/seaborn/SKILL.md` - Comprehensive seaborn documentation
+- `scientific-packages/seaborn/references/examples.md` - Practical use cases
+- `scientific-packages/seaborn/references/function_reference.md` - Complete API reference
+- `scientific-packages/seaborn/references/objects_interface.md` - Modern declarative API
+
 ### Plotly
 - Interactive figures for exploration
 - Export static images for publication