2020/10/30 by Zolotareva, Olga, Nasirigerdeh, Reza, Matschinske, Julian +9
#FOS: Biological sciences #Genomics (q-bio.GN) #Quantitative Methods (q-bio.QM)
paper · doi:10.48550/arxiv.2010.16403
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.