Publication:
HarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values

cris.virtual.departmentHigh Performance Computing
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cris.virtual.departmentHigh Performance Computing
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
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cris.virtual.departmentbrowseHigh Performance Computing
cris.virtual.departmentbrowseHigh Performance Computing
cris.virtual.departmentbrowseHigh Performance Computing
cris.virtual.departmentbrowseHigh Performance Computing
cris.virtualsource.department96eeb7e3-b287-4320-836e-6f62697f2214
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cris.virtualsource.department25ba2e6f-9989-47a4-aa6b-0908992396e8
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dc.contributor.authorVoß, Hannah
dc.contributor.authorSchlumbohm, Simon
dc.contributor.authorBarwikowski, Philip
dc.contributor.authorWurlitzer, Marcus
dc.contributor.authorDottermusch, Matthias
dc.contributor.authorNeumann, Philipp
dc.contributor.authorSchlüter, Hartmut
dc.contributor.authorNeumann, Julia E.
dc.contributor.authorKrisp, Christoph
dc.date.issued2022-06-20
dc.description.abstractDataset 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.
dc.description.versionNA
dc.identifier.doi10.1038/s41467-022-31007-x
dc.identifier.issn2041-1723
dc.identifier.issn2041-1723
dc.identifier.pmid35725563
dc.identifier.scopus2-s2.0-85132208409
dc.identifier.urihttps://openhsu.ub.hsu-hh.de/handle/10.24405/14377
dc.language.isoen
dc.relation.journalNature Communications
dc.relation.orgunitHigh Performance Computing
dc.relation.orgunitProjekt DEAL
dc.rights.accessRightsmetadata only access
dc.titleHarmonizR enables data harmonization across independent proteomic datasets with appropriate handling of missing values
dc.typeResearch article
dspace.entity.typePublication
hsu.peerReviewed
hsu.uniBibliography
oaire.citation.volume13
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