sortscore

Quickstart

Scoring Analysis Pipeline

Configuration

• See config.json and experiment_setup.csv for configuration file templates.

Usage

sortscore -c config.json

Example usage

Tile Normalization Pipeline

Configuration

• See batch_config.json for configuration file template.

Usage

sortscore -cb batch_config.json

Example usage

Python API

• All public functions use NumPy-style docstrings. See module docstrings and examples for API details.

TODO: test these example workflows

Loading Counts

   import pandas as pd
   # Load your data
   df = pd.read_csv('your_data.csv')

Calculating Activity scores

from sortscore.analysis.score import calculate_activity_scores

# Calculate activity scores. Average over replicates using a simple average, instead of default averaging weighted by variant read count.
scores = calculate_activity_scores([df], method='simple-avg')

Plotting

Histogram

from sortscore.visualization.plots import plot_activity_score_distribution
plot_activity_score_distribution(scores, score_col='avgscore')

Further Documentation

• For detailed installation, configuration, and usage instructions, see the docs/ folder:
  • Installation Guide
  • Usage Guide
  • Documentation Index

System Requirements

• Python 3.10+
• Bash shell

License

MIT