Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure.

ABSTRACT: Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the "Normics" algorithm: Proteins are ranked based on their expression level-corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE

SUBMITTER: Dressler FF 

PROVIDER: S-EPMC9450154 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature