PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection.

ABSTRACT:

Motivation

Integrating the knockoff framework with any variable-selection method delivers stringent false discovery rate (FDR) control without recourse to p-values, offering a powerful alternative for differential expression analysis of high-throughput omics datasets. However, existing knockoff generators rely on restrictive modelling assumptions or coarse approximations that often inflate the FDR when applied to real-world data.

Results

We introduce Partial Least Squares Knockoff (PLSKO), an efficient, assumption-free generator that remains robust across diverse omics platforms. Our extensive simulations show that PLSKO is the only method to maintain FDR control with sufficient power in complex non-linear settings. Our semi-simulation studies drawn from RNA-seq, proteomics,

SUBMITTER: Yang G 

PROVIDER: S-EPMC12449248 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature