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

Olga Zolotareva; Reza Nasirigerdeh; Julian Matschinske; Reihaneh Torkzadehmahani; Mohammad Bakhtiari; Tobias Frisch; Julian Späth; David B. Blumenthal; Amir Abbasinejad; Paolo Tieri; Georgios Kaissis; Daniel Rückert; Nina K. Wenke; Markus List; Jan Baumbach

doi:10.1186/s13059-021-02553-2

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

Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske, Reihaneh Torkzadehmahani, Mohammad Bakhtiari, Tobias Frisch, Julian Späth, David B. Blumenthal, Amir Abbasinejad, Paolo Tieri, Georgios Kaissis, Daniel Rückert, Nina K. Wenke, Markus List, Jan Baumbach

Assistant Professorship of Data Science in Systems Biology

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

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, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, 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.

Original language	English
Article number	338
Journal	Genome Biology
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s13059-021-02553-2
State	Published - Dec 2021

Keywords

Differential expression analysis
Federated learning
Meta-analysis
Privacy of biomedical data

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10.1186/s13059-021-02553-2

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Zolotareva, O., Nasirigerdeh, R., Matschinske, J., Torkzadehmahani, R., Bakhtiari, M., Frisch, T., Späth, J., Blumenthal, D. B., Abbasinejad, A., Tieri, P., Kaissis, G., Rückert, D., Wenke, N. K., List, M., & Baumbach, J. (2021). Flimma: a federated and privacy-aware tool for differential gene expression analysis. Genome Biology, 22(1), Article 338. https://doi.org/10.1186/s13059-021-02553-2

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abstract = "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, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, 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.",

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author = "Olga Zolotareva and Reza Nasirigerdeh and Julian Matschinske and Reihaneh Torkzadehmahani and Mohammad Bakhtiari and Tobias Frisch and Julian Sp{\"a}th and Blumenthal, {David B.} and Amir Abbasinejad and Paolo Tieri and Georgios Kaissis and Daniel R{\"u}ckert and Wenke, {Nina K.} and Markus List and Jan Baumbach",

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year = "2021",

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doi = "10.1186/s13059-021-02553-2",

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Zolotareva, O, Nasirigerdeh, R, Matschinske, J, Torkzadehmahani, R, Bakhtiari, M, Frisch, T, Späth, J, Blumenthal, DB, Abbasinejad, A, Tieri, P, Kaissis, G, Rückert, D, Wenke, NK, List, M & Baumbach, J 2021, 'Flimma: a federated and privacy-aware tool for differential gene expression analysis', Genome Biology, vol. 22, no. 1, 338. https://doi.org/10.1186/s13059-021-02553-2

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AU - Nasirigerdeh, Reza

AU - Matschinske, Julian

AU - Torkzadehmahani, Reihaneh

AU - Bakhtiari, Mohammad

AU - Frisch, Tobias

AU - Späth, Julian

AU - Blumenthal, David B.

AU - Abbasinejad, Amir

AU - Tieri, Paolo

AU - Kaissis, Georgios

AU - Rückert, Daniel

AU - Wenke, Nina K.

AU - List, Markus

AU - Baumbach, Jan

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AB - 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, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma (https://exbio.wzw.tum.de/flimma/) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, 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.

KW - Differential expression analysis

KW - Federated learning

KW - Meta-analysis

KW - Privacy of biomedical data

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ER -

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

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