direction_autocorrelation_analysis
A Python tool for analyzing directional persistence in cell migration and particle trajectories from fluorescence microscopy data.
Overview
Languages: Python
Documentation
Autocorrelation Analysis
A Python tool for analyzing directional persistence in cell migration and particle trajectories from fluorescence microscopy data.
Overview
This script performs autocorrelation analysis on trajectory data for fluorescently labeled proteins and cells tracked in microscope recordings. It calculates direction autocorrelation coefficients that quantify how well a cell maintains its direction of movement over time.
Key features: - Processes individual files or entire experimental datasets - Supports hierarchical data organization (file → condition → cross-condition analysis) - Creates detailed visualizations of persistence measurements - Analyzes individual tracks and condition-level aggregates - Compares different experimental conditions - Supports both Excel (.xlsx, .xls) and CSV (.csv) files
Installation
Requirements
- Python 3.6 or higher
- Required packages: numpy, pandas, matplotlib, tkinter
Data Requirements
The script works with cell trajectory data in CSV or Excel format with: - Frame numbers (time points) - X coordinates - Y coordinates
You can organize your data in different ways: 1. Single file with one or more sheets (conditions) 2. Condition folder containing multiple files (replicates) 3. Experiment directory containing multiple condition folders
Usage
From Spyder/IDE
Modify the CONFIG section at the top of the script:
```python
Basic parameters
INPUT_TIME_INTERVAL = 1.0 # Time between frames INPUT_NUM_INTERVALS = 25 # Number of time intervals to analyze
Choose ONE of these input modes:
INPUT_FILE = '/path/to/file.csv' # Single file mode
or
INPUT_CONDITION_DIR = '/path/to/condition_folder' # Condition folder mode
or
INPUT_EXPERIMENT_DIR = '/path/to/experiment_dir' # Experiment directory mode
Advanced options
SAVE_INDIVIDUAL_TRACKS = True # Save track-by-track data PLOT_INDIVIDUAL_TRACKS = True # Create plots with
[View full README on GitHub]