Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays.

ABSTRACT: Scientists use high-dimensional measurement assays to detect and prioritize regions of strong signal in spatially organized domain. Examples include finding methylation enriched genomic regions using microarrays, and active cortical areas using brain-imaging. The most common procedure for detecting potential regions is to group neighboring sites where the signal passed a threshold. However, one needs to account for the selection bias induced by this procedure to avoid diminishing effects when generalizing to a population. This paper introduces pin-down inference, a model and an inference framework that permit population inference for these detected regions. Pin-down inference provides non-asymptotic point and confidence interval estimators for the mean effect in the region that account for

SUBMITTER: Benjamini Y 

PROVIDER: S-EPMC9615469 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature