FridrichMethod Biomedical Data Visualization
Publication-Quality Settings
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
# Nature/Blood style settings
plt.rcParams.update({
'font.family': 'Arial',
'font.size': 8,
'axes.labelsize': 8,
'axes.titlesize': 9,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 7,
'figure.dpi': 300,
'savefig.dpi': 300,
'savefig.bbox': 'tight',
'axes.linewidth': 0.5,
'xtick.major.width': 0.5,
'ytick.major.width': 0.5,
})
# Color palettes
NATURE_COLORS = ['#E64B35', '#4DBBD5', '#00A087', '#3C5488', '#F39B7F', '#8491B4']
BLOOD_COLORS = ['#D62728', '#1F77B4', '#2CA02C', '#FF7F0E', '#9467BD', '#8C564B']
Volcano Plot
def volcano_plot(df, log2fc_col='log2FC', pval_col='pval_adj',
gene_col='gene', fc_thresh=1, pval_thresh=0.05,
highlight_genes=None, figsize=(4, 4)):
"""Publication-quality volcano plot."""
fig, ax = plt.subplots(figsize=figsize)
df = df.copy()
df['-log10pval'] = -np.log10(df[pval_col].clip(lower=1e-300))
# Categorize points
df['category'] = 'NS'
df.loc[(df[log2fc_col] > fc_thresh) & (df[pval_col] < pval_thresh), 'category'] = 'Up'
df.loc[(df[log2fc_col] < -fc_thresh) & (df[pval_col] < pval_thresh), 'category'] = 'Down'
colors = {'NS': '#CCCCCC', 'Up': '#E64B35', 'Down': '#4DBBD5'}
for cat, color in colors.items():
subset = df[df['category'] == cat]
ax.scatter(subset[log2fc_col], subset['-log10pval'],
c=color, s=10, alpha=0.7, edgecolors='none', label=cat)
# Add threshold lines
ax.axhline(-np.log10(pval_thresh), color='grey', linestyle='--', linewidth=0.5)
ax.axvline(-fc_thresh, color='grey', linestyle='--', linewidth=0.5)
ax.axvline(fc_thresh, color='grey', linestyle='--', linewidth=0.5)
# Label specific genes
if highlight_genes:
for gene in highlight_genes:
if gene in df[gene_col].values:
row = df[df[gene_col] == gene].iloc[0]
ax.annotate(gene, (row[log2fc_col], row['-log10pval']),
fontsize=6, ha='center')
ax.set_xlabel('log₂ Fold Change')
ax.set_ylabel('-log₁₀ Adjusted P-value')
ax.legend(frameon=False, loc='upper right')
plt.tight_layout()
return fig, ax
Heatmap with Clustering
import scipy.cluster.hierarchy as sch
from matplotlib.colors import LinearSegmentedColormap
def clustered_heatmap(data, row_labels=None, col_labels=None,
cmap='RdBu_r', center=0, figsize=(8, 10),
row_cluster=True, col_cluster=True):
"""Hierarchically clustered heatmap."""
# Clustering
if row_cluster:
row_linkage = sch.linkage(data, method='ward')
row_order = sch.dendrogram(row_linkage, no_plot=True)['leaves']
data = data[row_order, :]
if row_labels is not None:
row_labels = [row_labels[i] for i in row_order]
if col_cluster:
col_linkage = sch.linkage(data.T, method='ward')
col_order = sch.dendrogram(col_linkage, no_plot=True)['leaves']
data = data[:, col_order]
if col_labels is not None:
col_labels = [col_labels[i] for i in col_order]
fig, ax = plt.subplots(figsize=figsize)
im = ax.imshow(data, aspect='auto', cmap=cmap,
vmin=center-np.abs(data).max(), vmax=center+np.abs(data).max())
if row_labels:
ax.set_yticks(range(len(row_labels)))
ax.set_yticklabels(row_labels)
if col_labels:
ax.set_xticks(range(len(col_labels)))
ax.set_xticklabels(col_labels, rotation=45, ha='right')
plt.colorbar(im, ax=ax, shrink=0.5, label='Expression (z-score)')
plt.tight_layout()
return fig, ax
Scanpy Visualization Enhancements
import scanpy as sc
def enhanced_dotplot(adata, genes, groupby, figsize=(10, 8)):
"""Enhanced dot plot with proper visibility."""
sc.pl.dotplot(
adata, var_names=genes, groupby=groupby,
expression_cutoff=0.0001,
mean_only_expressed=False,
standard_scale='None',
smallest_dot=0.1,
dot_max=1.0,
cmap='Reds',
colorbar_title='Mean expression',
size_title='Fraction of cells (%)',
figsize=figsize,
show=False
)
plt.tight_layout()
return plt.gcf()
def multi_batch_umap(adata, color_by, batch_key='batch', figsize_per=(4, 4)):
"""UMAP plots per batch."""
batches = adata.obs[batch_key].unique()
n_batches = len(batches)
fig, axes = plt.subplots(1, n_batches,
figsize=(figsize_per[0]*n_batches, figsize_per[1]))
if n_batches == 1:
axes = [axes]
for ax, batch in zip(axes, batches):
adata_batch = adata[adata.obs[batch_key] == batch]
sc.pl.umap(adata_batch, color=color_by, ax=ax, show=False,
title=f'{batch}')
plt.tight_layout()
return fig
Statistical Annotation
from scipy import stats
def add_significance(ax, x1, x2, y, h, p_value):
"""Add significance bar to plot."""
ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], 'k-', linewidth=0.5)
if p_value < 0.0001:
sig = '****'
elif p_value < 0.001:
sig = '***'
elif p_value < 0.01:
sig = '**'
elif p_value < 0.05:
sig = '*'
else:
sig = 'ns'
ax.text((x1+x2)/2, y+h, sig, ha='center', va='bottom', fontsize=8)
Multi-Panel Figure Assembly
from matplotlib.gridspec import GridSpec
def create_figure_panel(n_rows, n_cols, width_ratios=None, height_ratios=None):
"""Create multi-panel figure."""
fig = plt.figure(figsize=(3*n_cols, 3*n_rows))
gs = GridSpec(n_rows, n_cols, figure=fig,
width_ratios=width_ratios or [1]*n_cols,
height_ratios=height_ratios or [1]*n_rows,
wspace=0.3, hspace=0.3)
axes = []
for i in range(n_rows):
row = []
for j in range(n_cols):
ax = fig.add_subplot(gs[i, j])
row.append(ax)
axes.append(row)
return fig, axes
def label_panels(axes, labels=None, fontsize=12, fontweight='bold'):
"""Add A, B, C... labels to panels."""
if labels is None:
labels = [chr(65+i) for i in range(len(axes))] # A, B, C...
for ax, label in zip(axes, labels):
ax.text(-0.15, 1.05, label, transform=ax.transAxes,
fontsize=fontsize, fontweight=fontweight, va='top')
Export for Journals
def save_figure(fig, filename, formats=['pdf', 'png', 'svg']):
"""Save in multiple formats for journals."""
for fmt in formats:
fig.savefig(f"{filename}.{fmt}", format=fmt, dpi=300,
bbox_inches='tight', facecolor='white', edgecolor='none')
print(f"Saved: {filename}.{{{'|'.join(formats)}}}")
See references/color_guidelines.md for accessibility standards.
See scripts/figure_templates.py for pre-built templates.