Universität zu Köln

Dressler, Franz F., Braegelmann, Johannes, Reischl, Markus and Perner, Sven (2022). Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure. Mol. Cell. Proteomics, 21 (9). AMSTERDAM: ELSEVIER. ISSN 1535-9484

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Abstract

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assump-tions. 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 ana-lyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the rela-tionship 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 likelihood of a protein in a given dataset. Subse-quent normalization is based on a subset of non-DE pro-teins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demon-strated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard vari-ance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected sub-sets of high DE likelihood.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Dressler, Franz F. UNSPECIFIED UNSPECIFIED UNSPECIFIED Braegelmann, Johannes UNSPECIFIED UNSPECIFIED UNSPECIFIED Reischl, Markus UNSPECIFIED UNSPECIFIED UNSPECIFIED Perner, Sven UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-690697
DOI:	10.1016/j.mcpro.2022.100269
Journal or Publication Title:	Mol. Cell. Proteomics
Volume:	21
Number:	9
Date:	2022
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1535-9484
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language QUANTITATIVE PROTEOMICS; ARRAY DATA; EXPRESSION; GENES; IDENTIFICATION; SQUARES; RATIO; TOOL Multiple languages Biochemical Research Methods Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69069