MultiDST 0.1.11
A toolkit for multiple hypothesis testing that implements common FWER and FDR control procedures, with intuitive significance visualisation using the Significant Index Plot.
License: MIT
Requires Python: >=3.10

# MultiDST Package


**Multiple Testing, Made Easy**


MultiDST is a Python library for large-scale multiple hypothesis testing. It provides a unified and user-friendly framework for controlling error rates when performing many simultaneous statistical tests. The package supports both family-wise error rate (FWER) and false discovery rate (FDR) control, making it suitable for applications such as genomics, biomedical studies, and other high-dimensional analyses.
A key feature of MultiDST is the **Significant Index Plot (SIP)**, which enables clear and intuitive visualisation of significant hypotheses under different correction methods.


## Implemented Methods

    Bonferroni Correction
        Reason for Selection: Baseline method; simple and conservative.
        Error Type: Controls the family-wise error rate (FWER).
        Reference: Bonferroni, C. (1935). Il calcolo delle assicurazioni su gruppi di teste. https://api.semanticscholar.org/CorpusID:89994272

    Holm-Bonferroni Correction
        Reason for Selection: Sequential approach; adjusts thresholds progressively.
        Error Type: Controls the family-wise error rate (FWER).
        Reference: Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. Source: Scandinavian Journal of Statistics Scand J Statist, 6(6), 65â€“70. http://www.jstor.org/stable/4615733%0Ahttp://www.jstor.org/page/info/about/policies/terms.jsp%0Ahttp://www.jstor.org

    Benjamini-Hochberg Procedure
        Reason for Selection: Balances power and control; widely used in genomics.
        Error Type: Controls the false discovery rate (FDR).
        Reference: Benjamini, Y., & Hochberg, Y. (1995). "Controlling the false discovery rate: A practical and powerful approach to multiple testing." Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289-300.

    Benjamini-Yekutieli Method
        Reason for Selection: FDR control under dependence; accounts for correlated tests.
        Error Type: Controls the false discovery rate (FDR).
        Reference: Benjamini, Y., & Yekutieli, D. (2001a). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165â€“1188. https://doi.org/10.1214/aos/1013699998

    Storey's Q Value
        Reason for Selection: Adaptive FDR control; estimates proportion of true null hypotheses.
        Error Type: Controls the false discovery rate (FDR).
        Reference: Storey, J. D., & Tibshirani, R. (2003). Statistical Significance for Genome-Wide Studies.

    SGoF Test (Sequential Goodness-of-Fit)
        Reason for Selection: Increased power for large-scale testing; combines p-values from multiple tests.
        Error Type: Varies (depends on setup).
        Reference: Carvajal-RodrÃ­guez, A., de UÃ±a-Alvarez, J., & RolÃ¡n-Alvarez, E. (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics, 10, 209. https://doi.org/10.1186/1471-2105-10-209

## Installation
You can install the MultiDST Package using pip:

```bash
pip install MultiDST_package
```

## Special Features
    Multiple Methods: Implements several established methods for multiple hypothesis testing, including Bonferroni correction, Holm-Bonferroni correction, Benjamini-Hochberg procedure, and more.

    Flexible Usage: Designed to handle different types of data and scenarios typically encountered in statistical analysis.

    Integration: Methods are integrated into a unified framework, making it easier to apply and compare different approaches within the same analysis.

    Enhancements: Includes novel enhancements such as a "hybrid" procedure and "multi-weighting" to improve robustness and flexibility in hypothesis testing.

    Visualization: Provides tools for visualizing significant hypotheses, aiding in the interpretation of results.