High-sensitivity pattern discovery in large, paired multiomic datasets.

ABSTRACT:

Motivation

Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control.

Results

Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships

SUBMITTER: Ghazi AR 

PROVIDER: S-EPMC9235493 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature