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)

Python - PCA

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

abundance = pd.read_csv('merged_abundance.txt', sep='\t', index_col=0).T

scaler = StandardScaler()
abundance_scaled = scaler.fit_transform(abundance)

pca = PCA(n_components=2)
pca_result = pca.fit_transform(abundance_scaled)

plt.figure(figsize=(8, 6))
plt.scatter(pca_result[:, 0], pca_result[:, 1])
for i, sample in enumerate(abundance.index):
    plt.annotate(sample, (pca_result[i, 0], pca_result[i, 1]))
plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
plt.title('PCA of Sample Composition')
plt.savefig('pca.png', dpi=300)

R - phyloseq Setup

Goal: Convert a MetaPhlAn merged abundance table into a phyloseq object for ecological analysis and visualization in R.

Approach: Filter to species-level rows, clean taxonomy names, build an OTU table and sample metadata data frame, and assemble into a phyloseq object.

library(phyloseq)
library(ggplot2)
library(vegan)

# From MetaPhlAn merged table
abundance <- read.table('merged_abundance.txt', sep = '\t', header = TRUE, row.names = 1)

# Filter to species level
species <- abundance[grepl('s__', rownames(abundance)), ]
rownames(species) <- sapply(strsplit(rownames(species), '\\|'), tail, 1)
rownames(species) <- gsub('s__', '', rownames(species))

# Create phyloseq object
otu <- otu_table(as.matrix(species), taxa_are_rows = TRUE)

# Sample metadata (create or load)
sample_data <- data.frame(
    Sample = colnames(species),
    Group = c('Control', 'Control', 'Treatment', 'Treatment'),
    row.names = colnames(species)
)
samp <- sample_data(sample_data)

ps <- phyloseq(otu, samp)

R - Stacked Bar Plot

library(phyloseq)
library(ggplot2)

# Top taxa
top_taxa <- names(sort(taxa_sums(ps), decreasing = TRUE))[1:10]
ps_top <- prune_taxa(top_taxa, ps)

# Stacked bar
plot_bar(ps_top, fill = 'Species') +
    geom_bar(stat = 'identity', position = 'stack') +
    theme_minimal() +
    labs(x = 'Sample', y = 'Relative Abundance (%)') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

R - Ordination (PCoA)

library(phyloseq)
library(ggplot2)

# Bray-Curtis distance
ord <- ordinate(ps, method = 'PCoA', distance = 'bray')

# Plot ordination
plot_ordination(ps, ord, color = 'Group') +
    geom_point(size = 4) +
    stat_ellipse() +
    theme_minimal() +
    labs(title = 'PCoA of Sample Composition')

R - Alpha Diversity

library(phyloseq)
library(ggplot2)

# Calculate diversity metrics
alpha_div <- estimate_richness(ps, measures = c('Shannon', 'Simpson', 'Observed'))

# Add metadata
alpha_div$Group <- sample_data(ps)$Group

# Plot
ggplot(alpha_div, aes(x = Group, y = Shannon, fill = Group)) +
    geom_boxplot() +
    geom_jitter(width = 0.1) +
    theme_minimal() +
    labs(title = 'Alpha Diversity by Group', y = 'Shannon Index')

R - Beta Diversity (PERMANOVA)

library(vegan)

# Get abundance matrix
abundance_matrix <- as(otu_table(ps), 'matrix')
if (taxa_are_rows(ps)) abundance_matrix <- t(abundance_matrix)

# Calculate Bray-Curtis distance
dist_bc <- vegdist(abundance_matrix, method = 'bray')

# PERMANOVA
groups <- sample_data(ps)$Group
permanova <- adonis2(dist_bc ~ groups, permutations = 999)
permanova

Krona Chart

# From Kraken2 report
ktImportTaxonomy -q 1 -t 5 kraken_report.txt -o krona_chart.html

# From MetaPhlAn
metaphlan2krona.py -p profile.txt -k krona_profile.txt
ktImportText krona_profile.txt -o krona_metaphlan.html

Key Packages

Python

R

Related Skills

• kraken-classification - Generate input data
• metaphlan-profiling - Generate input data
• abundance-estimation - Process Kraken output