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Sortscore Dependencies and References

Core Dependencies

Required Python Version

  • Python 3.11+: The package requires Python 3.11 or higher

Data Processing and Analysis

pandas (>=2.0.0)

  • Purpose: Data manipulation, CSV/TSV file handling, DataFrame operations
  • Usage: Primary data structure for scores, count data, and experiment results
  • Reference: McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 51-56.
  • URL: https://pandas.pydata.org/
  • Citation: pandas development team. (2020). pandas-dev/pandas: Pandas (v1.0.3). Zenodo. https://doi.org/10.5281/zenodo.3509134

numpy (>=1.24.0)

  • Purpose: Numerical computing, array operations, statistical calculations
  • Usage: Matrix operations, normalization, mathematical functions
  • Reference: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. (2020). Array programming with NumPy. Nature 585, 357–362.
  • URL: https://numpy.org/
  • Citation: Harris, C.R., et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2

Visualization

matplotlib (>=3.6.0)

  • Purpose: Core plotting library, figure generation, color management
  • Usage: Heatmap creation, scatter plots, figure export with transparent backgrounds
  • Reference: Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
  • URL: https://matplotlib.org/
  • Citation: J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.

seaborn (>=0.12.0)

  • Purpose: Statistical visualization, enhanced heatmap functionality
  • Usage: Heatmap generation with advanced styling, color normalization
  • Reference: Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021.
  • URL: https://seaborn.pydata.org/
  • Citation: Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021.

Bioinformatics

biopython (>=1.81)

  • Purpose: Sequence manipulation, DNA/protein sequence handling
  • Usage: DNA to amino acid translation, sequence parsing
  • Reference: Cock, P. J., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., ... & de Hoon, M. J. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422-1423.
  • URL: https://biopython.org/
  • Citation: Cock, P.J.A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009 Jun 1; 25(11):1422-3 pmid:19304878

hgvs (>=1.5.0)

  • Purpose: HGVS (Human Genome Variation Society) nomenclature parsing
  • Usage: Processing variant annotations in standard HGVS format
  • Reference: HGVS nomenclature for describing sequence variants
  • URL: https://github.com/biocommons/hgvs
  • Citation: den Dunnen JT, et al. HGVS Recommendations for the Description of Sequence Variants: 2016 Update. Hum Mutat. 2016 Jun;37(6):564-9. pmid:26931183

Statistical Analysis

scipy (imported as needed)

  • Purpose: Statistical functions, correlation analysis
  • Usage: Statistical testing in correlation analysis modules
  • Reference: Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & SciPy 1.0 Contributors. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods, 17(3), 261-272.
  • URL: https://scipy.org/
  • Citation: Pauli Virtanen, et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.

Development Dependencies

Standard Library Modules Used

  • argparse: Command-line argument parsing
  • json: JSON configuration file handling
  • logging: Comprehensive logging and audit trails
  • sys: System-specific parameters and functions
  • os: Operating system interface
  • pathlib: Object-oriented filesystem paths
  • typing: Type hints and annotations
  • dataclasses: Configuration data structures

Package Structure

Console Scripts

  • sortscore: Main entry point for single experiment analysis
  • sortscore.run_analysis: Alternative module invocation
  • sortscore.run_batch_analysis: Batch processing of multiple experiments

Key Modules

  • sortscore.analysis: Core analysis functions (scoring, normalization, filtering)
  • sortscore.visualization: Plotting and visualization functions
  • sortscore.utilities: Helper functions and utilities

Compatibility

Python Version Support

  • Python 3.11+: Required for modern type hints and performance improvements
  • Python 3.12: Fully supported and tested

Platform Support

  • Cross-platform: Works on Linux, macOS, and Windows
  • Architecture: Supports both x86_64 and ARM64 (Apple Silicon)

Installation

From Source

pip install -e .

Dependencies Installation

pip install -r requirements.txt

License and Attribution

This package builds upon the scientific Python ecosystem. When using sortscore in publications, please cite the relevant dependencies as listed above, particularly:

  • NumPy and pandas for data processing
  • matplotlib and seaborn for visualization
  • BioPython for sequence analysis
  • SciPy for statistical functions

Version History

  • v0.1.0: Initial release with core Sort-seq analysis functionality
  • Dependencies locked: Minimum versions specified for reproducibility
  • Active development: Regular updates with backwards compatibility maintained