BioProfiling.jl

BioProfiling.jl performs morphological profiling of high-content imaging screens in Julia by compiling, filtering, and statistically analyzing cell morphology measurements to identify perturbation effects and infer compound mechanisms of action.

Key Features:

• End-to-end profiling: Integrated workflow to compile, filter, and analyze morphological profiles derived from high-content imaging screens within the Julia environment.
• Data structures and helper functions: Robust data structures and functions to transform, normalize, and visualize morphological measurements.
• Robust statistical analysis: Statistical methods including distance metrics and permutation tests for quantifying significance and handling high fractions of outliers.
• Visual artifact diagnostics: Tools to detect and diagnose visual artifacts within morphological analyses.
• Biological integration: Support for analysis of chemical imaging screens and network localization of molecular targets to relate profiles to mechanisms of action.

Scientific Applications:

• High-content imaging studies: Generation and comparison of morphological profiles to detect cell state changes under diverse experimental conditions.
• Mechanism-of-action inference: Use of morphological signatures to provide insights into compound effects and mechanisms of action.
• Hypothesis testing and target localization: Statistical testing of perturbation effects and network localization of molecular targets for biological hypothesis generation.
• Drug discovery and chemical screening: Analysis of chemical imaging screens to prioritize compounds based on morphological impact.

Methodology:

Assembling morphological measurements into profiles and applying data structures and helper functions to transform, normalize, visualize, compile, filter, and analyze profiles; applying robust statistical methods including distance metrics and permutation tests to handle outliers; performing visual artifact diagnostics and network localization of molecular targets, implemented for computational efficiency in Julia.