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Projects / Pangenome Openness, Metabolic Pathways, and Biogeography / 02_comparative_analysis.ipynb

02 Comparative Analysis

Jupyter notebook from the Pangenome Openness, Metabolic Pathways, and Biogeography project.

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Comparative Analysis: Pangenome Openness, Pathways, and Geography

This notebook analyzes relationships between:

1. Pangenome openness (core vs. accessory genes)
2. Metabolic pathway diversity
3. Biogeographic distribution patterns

Prerequisites: Run 01_data_extraction.ipynb first to generate the integrated dataset.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from pathlib import Path

# Set plotting style
sns.set_style('whitegrid')
plt.rcParams['figure.figsize'] = (12, 8)

# Create figures directory
fig_dir = Path('../figures')
fig_dir.mkdir(exist_ok=True)

1. Load Integrated Dataset

# Load integrated dataset
data_dir = Path('../data')
df = pd.read_csv(data_dir / 'integrated_dataset.csv')

print(f"Total species: {len(df)}")
print(f"\nData availability:")
print(f"  Pangenome data: {len(df)} (100%)")
print(f"  Pathway data: {df['pathway_count'].notna().sum()} ({df['pathway_count'].notna().sum() / len(df) * 100:.1f}%)")
print(f"  Biogeography data: {df['n_genomes'].notna().sum()} ({df['n_genomes'].notna().sum() / len(df) * 100:.1f}%)")

# Filter for complete cases (all three data types)
df_complete = df[df[['pathway_count', 'n_genomes']].notna().all(axis=1)].copy()
print(f"\nComplete cases (all three data types): {len(df_complete)}")

df_complete.head()

Total species: 27690

Data availability:
  Pangenome data: 27690 (100%)
  Pathway data: 27690 (100.0%)
  Biogeography data: 2159 (7.8%)

Complete cases (all three data types): 2159

	gtdb_species_clade_id	GTDB_species	GTDB_taxonomy	no_genomes	no_core	no_aux_genome	no_singleton_gene_clusters	no_gene_clusters	mean_intra_species_ANI	ANI_circumscription_radius	...	partial_ratio	n_genomes	mean_lat	mean_lon	lat_range	lon_range	max_geodesic_dist_km	mean_geodesic_dist_km	max_embedding_dist	mean_embedding_dist
0	s__Staphylococcus_aureus--RS_GCF_001027105.1	s__Staphylococcus_aureus	d__Bacteria;p__Bacillota;c__Bacilli;o__Staphyl...	14526	2083	145831	86127.0	147914	98.76	95.0000	...	0.0	3822.0	24.804048	21.737536	100.746850	311.1964	19950.223137	8517.202820	NaN	NaN
1	s__Klebsiella_pneumoniae--RS_GCF_000742135.1	s__Klebsiella_pneumoniae	d__Bacteria;p__Pseudomonadota;c__Gammaproteoba...	14240	4199	438925	276743.0	443124	98.97	95.2390	...	0.0	4948.0	24.317957	44.043047	103.479176	310.9133	19941.671766	7954.427146	NaN	NaN
2	s__Salmonella_enterica--RS_GCF_000006945.2	s__Salmonella_enterica	d__Bacteria;p__Pseudomonadota;c__Gammaproteoba...	11402	3639	262732	149757.0	266371	98.82	95.0604	...	0.0	3208.0	24.504208	27.242457	124.952380	276.1451	19953.606023	8222.034417	NaN	NaN
3	s__Streptococcus_pneumoniae--RS_GCF_001457635.1	s__Streptococcus_pneumoniae	d__Bacteria;p__Bacillota;c__Bacilli;o__Lactoba...	8434	1475	115370	67191.0	116845	98.62	95.0000	...	0.0	532.0	27.668334	18.353585	94.674421	235.8000	18784.952385	7397.429695	1.591833	1.081545
4	s__Mycobacterium_tuberculosis--RS_GCF_000195955.2	s__Mycobacterium_tuberculosis	d__Bacteria;p__Actinomycetota;c__Actinomycetia...	6903	3741	139929	97549.0	143670	99.92	95.0000	...	0.0	2556.0	-6.971883	-37.978282	101.275879	302.9693	19910.410057	6327.529730	NaN	NaN

5 rows × 32 columns

2. Hypothesis 1: Pangenome Openness vs. Pathway Diversity

Hypothesis: Species with open pangenomes have greater metabolic pathway diversity.

# Correlation analysis
print("Correlation: Pangenome Openness vs. Pathway Diversity")
print("=" * 60)

# Calculate correlations
correlations = {
    'Accessory/Core Ratio': stats.pearsonr(df_complete['accessory_core_ratio'], df_complete['pathway_count']),
    'Singleton Ratio': stats.pearsonr(df_complete['singleton_ratio'], df_complete['pathway_count']),
    'Core Fraction': stats.pearsonr(df_complete['core_fraction'], df_complete['pathway_count']),
    'Pangenome Size': stats.pearsonr(df_complete['pangenome_size'], df_complete['pathway_count'])
}

for metric, (r, p) in correlations.items():
    print(f"{metric:25s}: r = {r:6.3f}, p = {p:.2e}")

print("\nNote: Control for genome count as covariate")
partial_corr = stats.pearsonr(
    df_complete['accessory_core_ratio'] / df_complete['no_genomes'],
    df_complete['pathway_count'] / df_complete['no_genomes']
)
print(f"Partial correlation (controlling for genome count): r = {partial_corr[0]:.3f}, p = {partial_corr[1]:.2e}")

Correlation: Pangenome Openness vs. Pathway Diversity
============================================================
Accessory/Core Ratio     : r =    nan, p = nan
Singleton Ratio          : r =    nan, p = nan
Core Fraction            : r =    nan, p = nan
Pangenome Size           : r =    nan, p = nan

Note: Control for genome count as covariate
Partial correlation (controlling for genome count): r = 0.461, p = 7.36e-114

/tmp/ipykernel_6115/1763985719.py:7: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Accessory/Core Ratio': stats.pearsonr(df_complete['accessory_core_ratio'], df_complete['pathway_count']),
/tmp/ipykernel_6115/1763985719.py:8: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Singleton Ratio': stats.pearsonr(df_complete['singleton_ratio'], df_complete['pathway_count']),
/tmp/ipykernel_6115/1763985719.py:9: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Core Fraction': stats.pearsonr(df_complete['core_fraction'], df_complete['pathway_count']),
/tmp/ipykernel_6115/1763985719.py:10: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Pangenome Size': stats.pearsonr(df_complete['pangenome_size'], df_complete['pathway_count'])

# Visualization: Scatter plots
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Pangenome Openness vs. Pathway Diversity', fontsize=16)

# Plot 1: Accessory/Core ratio
ax = axes[0, 0]
ax.scatter(df_complete['accessory_core_ratio'], df_complete['pathway_count'], 
           alpha=0.5, s=20)
ax.set_xlabel('Accessory/Core Gene Ratio')
ax.set_ylabel('Pathway Count')
ax.set_title(f"r = {correlations['Accessory/Core Ratio'][0]:.3f}")

# Plot 2: Singleton ratio
ax = axes[0, 1]
ax.scatter(df_complete['singleton_ratio'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='orange')
ax.set_xlabel('Singleton Gene Ratio')
ax.set_ylabel('Pathway Count')
ax.set_title(f"r = {correlations['Singleton Ratio'][0]:.3f}")

# Plot 3: Core fraction
ax = axes[1, 0]
ax.scatter(df_complete['core_fraction'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='green')
ax.set_xlabel('Core Gene Fraction')
ax.set_ylabel('Pathway Count')
ax.set_title(f"r = {correlations['Core Fraction'][0]:.3f}")

# Plot 4: Pangenome size
ax = axes[1, 1]
ax.scatter(df_complete['pangenome_size'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='red')
ax.set_xlabel('Pangenome Size (total gene clusters)')
ax.set_ylabel('Pathway Count')
ax.set_xscale('log')
ax.set_title(f"r = {correlations['Pangenome Size'][0]:.3f}")

plt.tight_layout()
plt.savefig(fig_dir / 'pangenome_vs_pathways.png', dpi=300, bbox_inches='tight')
plt.show()

3. Hypothesis 2: Geography vs. Pathway Diversity

Hypothesis: Species with broader biogeographic distributions have more diverse metabolic capabilities.

# Correlation analysis
print("Correlation: Biogeography vs. Pathway Diversity")
print("=" * 60)

geo_correlations = {
    'Max Geodesic Distance': stats.pearsonr(df_complete['max_geodesic_dist_km'], df_complete['pathway_count']),
    'Mean Geodesic Distance': stats.pearsonr(df_complete['mean_geodesic_dist_km'], df_complete['pathway_count']),
    'Max Embedding Distance': stats.pearsonr(df_complete['max_embedding_dist'], df_complete['pathway_count']),
    'Mean Embedding Distance': stats.pearsonr(df_complete['mean_embedding_dist'], df_complete['pathway_count']),
    'Latitude Range': stats.pearsonr(df_complete['lat_range'], df_complete['pathway_count']),
    'Longitude Range': stats.pearsonr(df_complete['lon_range'], df_complete['pathway_count'])
}

for metric, (r, p) in geo_correlations.items():
    print(f"{metric:30s}: r = {r:6.3f}, p = {p:.2e}")

Correlation: Biogeography vs. Pathway Diversity
============================================================
Max Geodesic Distance         : r =    nan, p = nan
Mean Geodesic Distance        : r =    nan, p = nan
Max Embedding Distance        : r =    nan, p = nan
Mean Embedding Distance       : r =    nan, p = nan
Latitude Range                : r =    nan, p = nan
Longitude Range               : r =    nan, p = nan

/tmp/ipykernel_6115/550706344.py:6: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Max Geodesic Distance': stats.pearsonr(df_complete['max_geodesic_dist_km'], df_complete['pathway_count']),
/tmp/ipykernel_6115/550706344.py:7: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Mean Geodesic Distance': stats.pearsonr(df_complete['mean_geodesic_dist_km'], df_complete['pathway_count']),
/tmp/ipykernel_6115/550706344.py:8: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Max Embedding Distance': stats.pearsonr(df_complete['max_embedding_dist'], df_complete['pathway_count']),
/tmp/ipykernel_6115/550706344.py:9: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Mean Embedding Distance': stats.pearsonr(df_complete['mean_embedding_dist'], df_complete['pathway_count']),
/tmp/ipykernel_6115/550706344.py:10: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Latitude Range': stats.pearsonr(df_complete['lat_range'], df_complete['pathway_count']),
/tmp/ipykernel_6115/550706344.py:11: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  'Longitude Range': stats.pearsonr(df_complete['lon_range'], df_complete['pathway_count'])

# Visualization
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Biogeographic Distribution vs. Pathway Diversity', fontsize=16)

# Plot 1: Geodesic distance
ax = axes[0, 0]
ax.scatter(df_complete['max_geodesic_dist_km'], df_complete['pathway_count'],
           alpha=0.5, s=20)
ax.set_xlabel('Max Geographic Distance (km)')
ax.set_ylabel('Pathway Count')
ax.set_title(f"r = {geo_correlations['Max Geodesic Distance'][0]:.3f}")
ax.set_xscale('log')

# Plot 2: Embedding distance
ax = axes[0, 1]
ax.scatter(df_complete['max_embedding_dist'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='orange')
ax.set_xlabel('Max AlphaEarth Embedding Distance')
ax.set_ylabel('Pathway Count')
ax.set_title(f"r = {geo_correlations['Max Embedding Distance'][0]:.3f}")

# Plot 3: Lat/Lon ranges
ax = axes[1, 0]
ax.scatter(df_complete['lat_range'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='green', label='Latitude')
ax.scatter(df_complete['lon_range'], df_complete['pathway_count'],
           alpha=0.5, s=20, color='red', label='Longitude')
ax.set_xlabel('Geographic Range (degrees)')
ax.set_ylabel('Pathway Count')
ax.set_title('Coordinate Ranges')
ax.legend()

# Plot 4: Present pathway ratio
ax = axes[1, 1]
ax.scatter(df_complete['max_geodesic_dist_km'], df_complete['present_ratio'],
           alpha=0.5, s=20, color='purple')
ax.set_xlabel('Max Geographic Distance (km)')
ax.set_ylabel('Pathway Present Ratio')
ax.set_xscale('log')
corr = stats.pearsonr(df_complete['max_geodesic_dist_km'], df_complete['present_ratio'])
ax.set_title(f"r = {corr[0]:.3f}")

plt.tight_layout()
plt.savefig(fig_dir / 'geography_vs_pathways.png', dpi=300, bbox_inches='tight')
plt.show()

/tmp/ipykernel_6115/3993817482.py:40: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  corr = stats.pearsonr(df_complete['max_geodesic_dist_km'], df_complete['present_ratio'])

4. Hypothesis 3: Pangenome Openness vs. Geographic Distribution

Hypothesis: Species with open pangenomes are more widely distributed geographically.

# Correlation analysis
print("Correlation: Pangenome Openness vs. Geographic Distribution")
print("=" * 60)

pan_geo_correlations = {
    'Accessory/Core vs. Geodesic Dist': stats.pearsonr(df_complete['accessory_core_ratio'], df_complete['max_geodesic_dist_km']),
    'Singleton Ratio vs. Geodesic Dist': stats.pearsonr(df_complete['singleton_ratio'], df_complete['max_geodesic_dist_km']),
    'Core Fraction vs. Geodesic Dist': stats.pearsonr(df_complete['core_fraction'], df_complete['max_geodesic_dist_km']),
    'Accessory/Core vs. Embedding Dist': stats.pearsonr(df_complete['accessory_core_ratio'], df_complete['max_embedding_dist']),
}

for metric, (r, p) in pan_geo_correlations.items():
    print(f"{metric:40s}: r = {r:6.3f}, p = {p:.2e}")

Correlation: Pangenome Openness vs. Geographic Distribution
============================================================
Accessory/Core vs. Geodesic Dist        : r =  0.382, p = 5.55e-76
Singleton Ratio vs. Geodesic Dist       : r =  0.378, p = 2.23e-74
Core Fraction vs. Geodesic Dist         : r = -0.494, p = 3.52e-133
Accessory/Core vs. Embedding Dist       : r =    nan, p = nan

# Visualization
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Pangenome Openness vs. Geographic Distribution', fontsize=16)

# Plot 1: Accessory/Core vs. Geodesic distance
ax = axes[0, 0]
ax.scatter(df_complete['accessory_core_ratio'], df_complete['max_geodesic_dist_km'],
           alpha=0.5, s=20)
ax.set_xlabel('Accessory/Core Gene Ratio')
ax.set_ylabel('Max Geographic Distance (km)')
ax.set_yscale('log')
ax.set_title(f"r = {pan_geo_correlations['Accessory/Core vs. Geodesic Dist'][0]:.3f}")

# Plot 2: Core fraction vs. Geodesic distance
ax = axes[0, 1]
ax.scatter(df_complete['core_fraction'], df_complete['max_geodesic_dist_km'],
           alpha=0.5, s=20, color='orange')
ax.set_xlabel('Core Gene Fraction')
ax.set_ylabel('Max Geographic Distance (km)')
ax.set_yscale('log')
ax.set_title(f"r = {pan_geo_correlations['Core Fraction vs. Geodesic Dist'][0]:.3f}")

# Plot 3: Accessory/Core vs. Embedding distance
ax = axes[1, 0]
ax.scatter(df_complete['accessory_core_ratio'], df_complete['max_embedding_dist'],
           alpha=0.5, s=20, color='green')
ax.set_xlabel('Accessory/Core Gene Ratio')
ax.set_ylabel('Max AlphaEarth Embedding Distance')
ax.set_title(f"r = {pan_geo_correlations['Accessory/Core vs. Embedding Dist'][0]:.3f}")

# Plot 4: 3D relationship
from mpl_toolkits.mplot3d import Axes3D
ax = fig.add_subplot(224, projection='3d')
scatter = ax.scatter(df_complete['accessory_core_ratio'], 
                     df_complete['pathway_count'],
                     df_complete['max_geodesic_dist_km'],
                     c=df_complete['no_genomes'], cmap='viridis',
                     alpha=0.6, s=20)
ax.set_xlabel('Accessory/Core')
ax.set_ylabel('Pathways')
ax.set_zlabel('Geodesic Dist (km)')
ax.set_zscale('log')
plt.colorbar(scatter, ax=ax, label='# Genomes', shrink=0.5)

plt.tight_layout()
plt.savefig(fig_dir / 'pangenome_vs_geography.png', dpi=300, bbox_inches='tight')
plt.show()

/tmp/ipykernel_6115/138554714.py:42: UserWarning: Attempt to set non-positive zlim on a log-scaled axis will be ignored.
  ax.set_zscale('log')

5. Stratified Analysis by Phylum

Do patterns differ across major bacterial clades?

# Extract phylum from GTDB taxonomy
df_complete['phylum'] = df_complete['GTDB_taxonomy'].str.split(';').str[1]

# Count species per phylum
phylum_counts = df_complete['phylum'].value_counts()
print("Species per phylum (top 10):")
print(phylum_counts.head(10))

# Filter for phyla with ≥20 species
major_phyla = phylum_counts[phylum_counts >= 20].index
df_phyla = df_complete[df_complete['phylum'].isin(major_phyla)]
print(f"\nAnalyzing {len(major_phyla)} phyla with ≥20 species each")

Species per phylum (top 10):
phylum
p__Pseudomonadota      666
p__Bacteroidota        239
p__Bacillota_A         218
p__Actinomycetota      195
p__Bacillota           176
p__Patescibacteria     118
p__Cyanobacteriota      49
p__Chloroflexota        46
p__Desulfobacterota     35
p__Acidobacteriota      32
Name: count, dtype: int64

Analyzing 18 phyla with ≥20 species each

# Correlation by phylum
print("\nCorrelations by Phylum: Accessory/Core Ratio vs. Pathway Count")
print("=" * 70)
print(f"{'Phylum':<40s} {'N':>5s} {'r':>8s} {'p-value':>12s}")
print("-" * 70)

for phylum in major_phyla:
    subset = df_phyla[df_phyla['phylum'] == phylum]
    if len(subset) >= 10:  # Need sufficient data
        r, p = stats.pearsonr(subset['accessory_core_ratio'], subset['pathway_count'])
        print(f"{phylum:<40s} {len(subset):>5d} {r:>8.3f} {p:>12.2e}")

Correlations by Phylum: Accessory/Core Ratio vs. Pathway Count
======================================================================
Phylum                                       N        r      p-value
----------------------------------------------------------------------
p__Pseudomonadota                          666      nan          nan
p__Bacteroidota                            239      nan          nan
p__Bacillota_A                             218      nan          nan
p__Actinomycetota                          195      nan          nan
p__Bacillota                               176      nan          nan
p__Patescibacteria                         118      nan          nan
p__Cyanobacteriota                          49      nan          nan
p__Chloroflexota                            46      nan          nan
p__Desulfobacterota                         35      nan          nan
p__Acidobacteriota                          32      nan          nan
p__Campylobacterota                         31      nan          nan
p__Verrucomicrobiota                        29      nan          nan
p__Thermoproteota                           29      nan          nan
p__Spirochaetota                            26      nan          nan
p__Bacillota_C                              22      nan          nan
p__Nanoarchaeota                            22      nan          nan
p__Thermoplasmatota                         21      nan          nan
p__Planctomycetota                          20      nan          nan

/tmp/ipykernel_6115/1650922385.py:10: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.
  r, p = stats.pearsonr(subset['accessory_core_ratio'], subset['pathway_count'])

# Visualization: Box plots by phylum
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
fig.suptitle('Pangenome and Pathway Metrics by Phylum', fontsize=16)

# Select top 5 phyla for clarity
top_phyla = phylum_counts.head(5).index
df_top = df_complete[df_complete['phylum'].isin(top_phyla)]

# Box plot 1: Accessory/Core ratio
ax = axes[0]
df_top.boxplot(column='accessory_core_ratio', by='phylum', ax=ax)
ax.set_xlabel('Phylum')
ax.set_ylabel('Accessory/Core Ratio')
ax.set_title('Pangenome Openness')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.sca(ax)
plt.xticks(rotation=45, ha='right')

# Box plot 2: Pathway count
ax = axes[1]
df_top.boxplot(column='pathway_count', by='phylum', ax=ax)
ax.set_xlabel('Phylum')
ax.set_ylabel('Pathway Count')
ax.set_title('Metabolic Diversity')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.sca(ax)
plt.xticks(rotation=45, ha='right')

# Box plot 3: Geographic range
ax = axes[2]
df_top.boxplot(column='max_geodesic_dist_km', by='phylum', ax=ax)
ax.set_xlabel('Phylum')
ax.set_ylabel('Max Geographic Distance (km)')
ax.set_yscale('log')
ax.set_title('Biogeographic Range')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.sca(ax)
plt.xticks(rotation=45, ha='right')

plt.tight_layout()
plt.savefig(fig_dir / 'phylum_stratified_analysis.png', dpi=300, bbox_inches='tight')
plt.show()

6. Summary and Interpretation

print("=" * 70)
print("SUMMARY: Pangenome-Pathway-Geography Analysis")
print("=" * 70)

print(f"\nDataset: {len(df_complete)} species with complete data")

print("\n1. Pangenome vs. Pathways:")
r, p = correlations['Accessory/Core Ratio']
direction = "positive" if r > 0 else "negative"
strength = "strong" if abs(r) > 0.5 else "moderate" if abs(r) > 0.3 else "weak"
print(f"   {strength.capitalize()} {direction} correlation (r = {r:.3f}, p = {p:.2e})")

print("\n2. Geography vs. Pathways:")
r, p = geo_correlations['Max Geodesic Distance']
direction = "positive" if r > 0 else "negative"
strength = "strong" if abs(r) > 0.5 else "moderate" if abs(r) > 0.3 else "weak"
print(f"   {strength.capitalize()} {direction} correlation (r = {r:.3f}, p = {p:.2e})")

print("\n3. Pangenome vs. Geography:")
r, p = pan_geo_correlations['Accessory/Core vs. Geodesic Dist']
direction = "positive" if r > 0 else "negative"
strength = "strong" if abs(r) > 0.5 else "moderate" if abs(r) > 0.3 else "weak"
print(f"   {strength.capitalize()} {direction} correlation (r = {r:.3f}, p = {p:.2e})")

print("\n" + "=" * 70)
print("\nFigures saved to ../figures/")
print("  - pangenome_vs_pathways.png")
print("  - geography_vs_pathways.png")
print("  - pangenome_vs_geography.png")
print("  - phylum_stratified_analysis.png")

======================================================================
SUMMARY: Pangenome-Pathway-Geography Analysis
======================================================================

Dataset: 2159 species with complete data

1. Pangenome vs. Pathways:
   Weak negative correlation (r = nan, p = nan)

2. Geography vs. Pathways:
   Weak negative correlation (r = nan, p = nan)

3. Pangenome vs. Geography:
   Moderate positive correlation (r = 0.382, p = 5.55e-76)

======================================================================

Figures saved to ../figures/
  - pangenome_vs_pathways.png
  - geography_vs_pathways.png
  - pangenome_vs_geography.png
  - phylum_stratified_analysis.png

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