Flimma: a federated and privacy-aware tool for differential gene expression analysis
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AbstractAggregating 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.
2013 ◽
Vol 12
(4)
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pp. 157-169
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2020 ◽
Vol 228
(1)
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pp. 43-49
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2019 ◽
Vol 227
(1)
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pp. 64-82
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2019 ◽
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2020 ◽
Vol 56
(6)
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pp. 577-586