PROTRIDER: protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder.

ABSTRACT:

Motivation

Detection of gene regulatory aberrations enhances our ability to interpret the impact of inherited and acquired genetic variation for rare disease diagnostics and tumor characterization. While numerous methods for calling RNA expression outliers from RNA-sequencing data have been proposed, the establishment of protein expression outliers from mass spectrometry data is lacking.

Results

Here, we propose and assess various modeling approaches to call protein expression outliers across three datasets from rare disease diagnostics and oncology. We use as independent evidence the enrichment for outlier calls in matched RNA-seq samples and the enrichment for rare variants likely disrupting protein expression. We show that controlling for hidden confounders and technical

SUBMITTER: Klaproth-Andrade D 

PROVIDER: S-EPMC12931418 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature