PyViscount: Validating False Discovery Rate Estimation Methods via Random Search Space Partition.

ABSTRACT: Validating false discovery rate (FDR) estimation is an essential but surprisingly understudied aspect of method development in shotgun proteomics. Currently available validation protocols mostly rely on ground truth data sets, which typically involve manipulating the properties of the search space or query spectra used. As a result, comparing estimated FDR and ground truth-based false discovery proportion values may not be representative of the scenarios involving natural data sets encountered in practice. In this study, we introduce PyViscount─a Python tool implementing a novel validation protocol based on random search space partition, which enables generating a quasi ground-truth using unaltered search spaces of unique candidate peptides and generic data sets of experimental query spect

SUBMITTER: Madej D 

PROVIDER: S-EPMC11894659 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature