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OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery

  • 0Eric and Wendy Schmidt Center, Broad Institute, Cambridge, MA 02142.
Proceedings of the National Academy of Sciences of the United States of America +

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April 2, 2024

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April 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new statistical test, Optimized Adaptive Statistic for Inferring Structure (OASIS), offers efficient and valid analysis for contingency tables. OASIS excels in genomic data analysis, enabling novel strain detection and outperforming existing methods in simulations.

Area Of Science

  • Statistics
  • Genomics
  • Bioinformatics

Background

  • Contingency tables are crucial in quantitative research but existing statistical tests lack computational efficiency and statistical validity for finite samples.
  • Reference-free genomic inference presents challenges requiring robust statistical methods for analyzing count data.

Purpose Of The Study

  • To develop a novel family of statistical tests, Optimized Adaptive Statistic for Inferring Structure (OASIS), for contingency tables.
  • To provide computationally efficient and statistically valid tests suitable for finite observations, particularly in genomic applications.

Main Methods

  • OASIS constructs a test statistic linear in the normalized data matrix, utilizing concentration inequalities for closed-form P-value bounds.
  • Derivation of the asymptotic distribution of the OASIS test statistic to validate finite-sample bounds.
  • Application of OASIS to genomic sequencing data for strain detection and analysis of overdispersed data.

Main Results

  • OASIS provides interpretable decomposition of contingency tables, aiding in the understanding of null hypothesis rejection.
  • Experiments demonstrate OASIS's power and interpretability in genomic data, enabling de novo detection of SARS-CoV-2 and Mycobacterium tuberculosis strains.
  • Simulations show OASIS is robust to overdispersion, controls false discovery rate effectively, and outperforms Pearson's chi-squared test in certain scenarios.

Conclusions

  • OASIS represents a significant advancement in statistical testing for contingency tables, offering improved efficiency and validity.
  • The method facilitates novel applications in genomics, such as identifying microbial strains, which were previously unachievable.
  • OASIS demonstrates superior performance and robustness compared to traditional methods like Pearson's chi-squared test, especially for complex biological data.

Keywords:

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