Flimma: a federated and privacy-aware tool for differential gene expression analysis
Zolotareva O, Nasirigerdeh R, Matschinske J, Torkzadehmahani R, Bakhtiari M, Frisch T, Spaeth J, Blumenthal DB, Abbasinejad A, Tieri P, Kaissis G, Rueckert D, Wenke NK, List M, Baumbach J (2021)
Publication Language: English
Publication Status: Published
Publication Type: Journal article
Publication year: 2021
Journal
Publisher: BMC
Book Volume: 22
Article Number: 338
Journal Issue: 1
DOI: 10.1186/s13059-021-02553-2
Abstract
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
Authors with CRIS profile
Involved external institutions
Universität Hamburg (UHH)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Università degli studi "La Sapienza"
Italy (IT)
Consiglio Nazionale delle Ricerche (CNR) / National Research Council of Italy
Italy (IT)
University of Southern Denmark / Syddansk Universitet
Denmark (DK)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Università degli studi "La Sapienza"
Italy (IT)
Consiglio Nazionale delle Ricerche (CNR) / National Research Council of Italy
Italy (IT)
University of Southern Denmark / Syddansk Universitet
Denmark (DK)
How to cite
APA:
Zolotareva, O., Nasirigerdeh, R., Matschinske, J., Torkzadehmahani, R., Bakhtiari, M., Frisch, T.,... Baumbach, J. (2021). Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biology, 22(1). https://doi.org/10.1186/s13059-021-02553-2
MLA:
Zolotareva, Olga, et al. "Flimma: a federated and privacy-aware tool for differential gene expression analysis." Genome Biology 22.1 (2021).
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