Selective Inference for Hierarchical Clustering.
J Am Stat Assoc
; 119(545): 332-342, 2024.
Article
in En
| MEDLINE
| ID: mdl-38660582
ABSTRACT
Classical tests for a difference in means control the type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated type I error rate. Notably, this problem persists even if two separate and independent data sets are used to define the groups and to test for a difference in their means. To address this problem, in this paper, we propose a selective inference approach to test for a difference in means between two clusters. Our procedure controls the selective type I error rate by accounting for the fact that the choice of null hypothesis was made based on the data. We describe how to efficiently compute exact p-values for clusters obtained using agglomerative hierarchical clustering with many commonly-used linkages. We apply our method to simulated data and to single-cell RNA-sequencing data.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
J Am Stat Assoc
/
J. am. stat. assoc
/
Journal of the american statistical association
Year:
2024
Document type:
Article
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