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.
@article{arxiv.2010.16403,
title = {Flimma: a federated and privacy-preserving tool for differential gene expression analysis},
author = {Olga Zolotareva and Reza Nasirigerdeh and Julian Matschinske and Reihaneh Torkzadehmahani and Tobias Frisch and Julian Späth and David B. Blumenthal and Amir Abbasinejad and Paolo Tieri and Nina K. Wenke and Markus List and Jan Baumbach},
journal= {arXiv preprint arXiv:2010.16403},
year = {2020}
}