bio-metagenomics-visualization

Version Compatibility

Reference examples tested with: MetaPhlAn 4.1+, ggplot2 3.5+, matplotlib 3.8+, pandas 2.2+, phyloseq 1.46+, scanpy 1.10+, scikit-learn 1.4+, scipy 1.12+, seaborn 0.13+, vegan 2.6+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Metagenome Visualization

"Visualize the taxonomic composition of my metagenomes" → Create publication-quality figures (stacked bars, heatmaps, ordination plots) from taxonomic profiling output to compare community composition across samples.

• R: phyloseq::plot_bar(), microbiome package
• Python: matplotlib/seaborn with pandas for custom compositions

Python - Stacked Bar Plot

import pandas as pd
import matplotlib.pyplot as plt

abundance = pd.read_csv('merged_abundance.txt', sep='\t', index_col=0)
abundance = abundance[abundance.index.str.contains('s__')]
abundance.index = abundance.index.str.split('|').str[-1].str.replace('s__', '')

top_n = 10
top_species = abundance.sum(axis=1).nlargest(top_n).index
abundance_top = abundance.loc[top_species]
abundance_top.loc['Other'] = abundance.drop(top_species).sum()

abundance_top.T.plot(kind='bar', stacked=True, figsize=(12, 6), colormap='tab20')
plt.xlabel('Sample')
plt.ylabel('Relative Abundance (%)')
plt.title('Species Composition')
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left')
plt.tight_layout()
plt.savefig('stacked_bar.png', dpi=300)

Python - Heatmap

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

abundance = pd.read_csv('merged_abundance.txt', sep='\t', index_col=0)
abundance = abundance[abundance.index.str.contains('s__')]
abundance.index = abundance.index.str.split('|').str[-1].str.replace('s__', '')

top_species = abundance.sum(axis=1).nlargest(20).index
abundance_top = abundance.loc[top_species]

plt.figure(figsize=(12, 10))
sns.heatmap(abundance_top, cmap='YlOrRd', annot=False, cbar_kws={'label': 'Abundance (%)'})
plt.xlabel('Sample')
plt.ylabel('Species')
plt.title('Species Abundance Heatmap')
plt.tight_layout()
plt.savefig('heatmap.png', dpi=300)