pyphe

pyphe analyzes high-throughput microbial colony images to quantify colony sizes, phloxine B–derived viability scores, and growth-curve metrics for microbial fitness assessment.

Key Features:

• Automation and Integration: Automates the workflow from image acquisition to statistical analysis for large-scale colony screens.
• Versatile Data Processing: Analyzes colony sizes, viability scores derived from phloxine B staining, and growth curves including maximum growth slopes from time-series data.
• Image Acquisition Support: Supports image acquisition using transilluminating flatbed scanners for colony imaging.
• Quantification of Viability: Quantifies colony redness for phloxine B–based viability scoring to estimate the fraction of live cells within colonies.
• Data Normalization and Statistical Analysis: Implements data normalization and statistical analysis methods to derive fitness metrics and compare conditions and genetic backgrounds.
• Complementary Metrics Insight: Enables comparison showing that late endpoint colony-size measurements can provide insights similar to time-series maximum growth slopes and that colony size and viability scores are complementary readouts.

Scientific Applications:

• Large-scale phenotyping: Applied to phenotype gene-deletion strains of fission yeast across 59,350 individual fitness assays under 70 different conditions.
• Viability assessment: Uses phloxine B–derived viability scores to reflect the fraction of live cells within colonies for microbial fitness studies.

Methodology:

Quantification of colony sizes and colony redness for phloxine B viability scoring; data normalization to account for experimental variability; and statistical analysis to derive fitness metrics and compare conditions or genetic backgrounds.