HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values
Publication date
2022-06-20
Document type
Research article
Author
Voß, Hannah
Barwikowski, Philip
Wurlitzer, Marcus
Dottermusch, Matthias
Schlüter, Hartmut
Neumann, Julia E.
Krisp, Christoph
Organisational unit
Projekt DEAL
Scopus ID
Pubmed ID
Series or journal
Nature Communications
Periodical volume
13
Peer-reviewed
✅
Part of the university bibliography
✅
Abstract
Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Proteome datasets display high technical variability and frequent missing values. Sophisticated strategies for batch effect reduction are lacking or rely on error-prone data imputation. Here we introduce HarmonizR, a data harmonization tool with appropriate missing value handling. The method exploits the structure of available data and matrix dissection for minimal data loss, without data imputation. This strategy implements two common batch effect reduction methods-ComBat and limma (removeBatchEffect()). The HarmonizR strategy, evaluated on four exemplarily analyzed datasets with up to 23 batches, demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches. Compared to data imputation methods, HarmonizR was more efficient and performed superior regarding the detection of significant proteins. HarmonizR is an efficient tool for missing data tolerant experimental variance reduction and is easily adjustable for individual dataset properties and user preferences.
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