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Aggregating transcriptomics data across hospitals can increase sensitivityand robustness of differential expression analyses, yielding deeper clinicalinsights. As data exchange is often restricted by privacy legislation,meta-analyses are frequently employed to pool local results. However, if classlabels are inhomogeneously distributed between cohorts, their accuracy maydrop. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue byimplementing the state-of-the-art workflow limma voom in a privacy-preservingmanner, i.e. patient data never leaves its source site. Flimma results areidentical to those generated by limma voom on combined datasets even inimbalanced scenarios where meta-analysis approaches fail.

Flimma: a federated and privacy-preserving tool for differential gene expression analysis