  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, if class
labels are inhomogeneously distributed between cohorts, their accuracy may
drop. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by
implementing the state-of-the-art workflow limma voom in a privacy-preserving
manner, i.e. patient data never leaves its source site. Flimma results are
identical to those generated by limma voom on combined datasets even in
imbalanced scenarios where meta-analysis approaches fail.
