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

Aggregating clinical transcriptomics data across hospitals can increase sensitivity and robustness of differential gene 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, if class labels or confounders are inhomogeneously distributed between cohorts, their accuracy may drop significantly. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art gene expression workflow limma voom in a federated, privacy-preserving manner. Flimma results are identical to those generated by limma voom on combined datasets even in imbalanced scenarios where meta-analysis approaches fail.