05 Fitness Analysis
Jupyter notebook from the Antibiotic Resistance Hotspots in Microbial Pangenomes project.
Phase 5: Fitness Trade-off Analysis¶
This notebook uses the Fitness Browser data to understand the fitness costs of carrying ARGs:
- Cross-reference ARG genes with fitness data
- Calculate fitness effects in experimental conditions
- Analyze trade-offs between resistance and growth
In [ ]:
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
print("Phase 5: Fitness Analysis")
Access Fitness Browser Data¶
In [ ]:
# TODO: Query kescience_fitnessbrowser collection
# Explore tables: fitness, fitness_experiments, and related tables
fitness_query = """
SELECT * FROM kescience_fitnessbrowser.fitness LIMIT 5
"""
print("Fitness data query prepared")
Match ARGs to Fitness Data¶
In [ ]:
# TODO: Cross-reference ARG genes with fitness gene IDs
# Handle different gene ID formats (may require orthologue matching)
print("ARG-fitness matching in progress...")
Calculate Fitness Effects¶
In [ ]:
# TODO: For each ARG in fitness data:
# - Extract fitness scores across conditions
# - Calculate mean fitness and variance
# - Identify conditions with strong fitness effects
# - Compare ARG fitness to background distribution
print("Fitness effect calculations in progress...")
Trade-off Analysis¶
In [ ]:
# TODO: Analyze whether:
# - Genes in high-prevalence ARGs show significant fitness costs
# - Fitness costs vary across experimental conditions
# - There's evidence of compensatory mutations
print("Trade-off analysis in progress...")