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
Publisher: BMC
Book Volume: 22
Article Number: 338
Journal Issue: 1
DOI: 10.1186/s13059-021-02553-2
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.
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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