Turn metagenomic profiles, which describe microbial composition, into figures using R or Python. Create stacked bars, heatmaps, and sample comparison plots, then examine diversity within samples and differences between groups.
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Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
phyloseq::plot_bar(), microbiome packagematplotlib/seaborn with pandas for custom compositionsimport 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)
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)
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)
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)
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))
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')
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')
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
# 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
| Package | Purpose |
|---|---|
| matplotlib | General plotting |
| seaborn | Statistical visualizations |
| scikit-learn | PCA, clustering |
| scipy | Statistical tests |
| Package | Purpose |
|---|---|
| phyloseq | Microbiome data handling |
| vegan | Community ecology |
| ggplot2 | Visualization |
| microbiome | Additional analyses |
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