Digital Total - Computing & Data Science an der Universität Hamburg und in der Wissenschaftsmetropole Hamburg

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

83

Nicht eingeplant

20m

Von-Melle-Park 4

Poster

Beschreibung

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differen-
tial expression analyses, yielding deeper clinical insights. As data exchange is often restricted by pri-
vacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy
might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://featurecloud.ai/app/flimma)
addresses this issue by implementing the state-of-the-art workflow limma voom in a federated man-
ner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by
limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches
fail

Keywords

Federated Learning
Differential Gene Expression
Privacy-Preserving learning
SMPC