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 URL 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.
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 URL 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.
D
clinical
R
gene expression analyses
expression analyses,
D
or confounders
D
significantly
D
gene expression
D
federated,
A
, i.e. patient data never leaves its source site