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DOI	10.1073/pnas.2304671121
	OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery
	Baharav, Tavor Z.; Tse, David; Salzman, Julia
通讯作者	Salzman, J
来源期刊	PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN	0027-8424
EISSN	1091-6490
出版年	2024
卷号	121 期号:15
英文摘要	Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference [K. Chaung et al., Cell 186, 5440-5456 (2023)], we develop Optimized Adaptive Statistic for Inferring Structure (OASIS), a family of statistical tests for contingency tables. OASIS constructs a test statistic which is linear in the normalized data matrix, providing closed-form P-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finite-sample bounds correctly characterize the test statistic's P-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. Using OASIS, we develop a method that can detect SARS-CoV-2 and Mycobacterium tuberculosis strains de novo, which existing approaches cannot achieve. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single-cell RNA sequencing, where under accepted noise models OASIS provides good control of the false discovery rate, while Pearson's X2 consistently rejects the null. Additionally, we show in simulations that OASIS is more powerful than Pearson's X2 in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations.
英文关键词	computational genomics reference genome free inference contingency table finite-sample P-value
类型	Article
语种	英语
开放获取类型	Green Published, hybrid
收录类别	SCI-E
WOS记录号	WOS:001207680000004
WOS关键词	CHI-SQUARE
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405230
推荐引用方式 GB/T 7714	Baharav, Tavor Z.,Tse, David,Salzman, Julia. OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery[J],2024,121(15).
APA	Baharav, Tavor Z.,Tse, David,&Salzman, Julia.(2024).OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,121(15).
MLA	Baharav, Tavor Z.,et al."OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 121.15(2024).