Testing for a difference in means of a single feature after clustering.
| Autor: | Chen YT; Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, United States., Gao LL; Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC V6T 1Z4, Canada. |
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| Jazyk: | angličtina |
| Zdroj: | Biostatistics (Oxford, England) [Biostatistics] 2024 Dec 31; Vol. 26 (1). |
| DOI: | 10.1093/biostatistics/kxae046 |
| Abstrakt: | For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common interpretation and validation approach involves testing differences in feature means between observations in two estimated clusters. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we propose a new test for the difference in means in a single feature between a pair of clusters obtained using hierarchical or k-means clustering. The test controls the selective Type I error rate in finite samples and can be efficiently computed. We further illustrate the validity and power of our proposal in simulation and demonstrate its use on single-cell RNA-sequencing data. (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.) |
| Databáze: | MEDLINE |
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