Flimma: a federated and privacy-aware tool for differential gene expression analysis
OA Location
Author(s)
Zolotareva, Olga
Nasirigerdeh, Reza
Matschinske, Julian
Torkzadehmahani, Reihaneh
Bakhtiari, Mohammad
Type
Journal Article
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.
Date Issued
2021-12-14
Date Acceptance
2021-11-22
Citation
Genome Biology, 2021, 22 (1)
ISSN
1474-7596
Publisher
BMC
Journal / Book Title
Genome Biology
Volume
22
Issue
1
Copyright Statement
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative
Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated in a credit line to the data.
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative
Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated in a credit line to the data.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000730207600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
Genetics & Heredity
Differential expression analysis
Federated learning
Privacy of biomedical data
Meta-analysis
RNA-SEQ
BIOCONDUCTOR PACKAGE
QUALITY-CONTROL
METAANALYSIS
REPRODUCIBILITY
QUANTIFICATION
GENOTYPES
SAMPLES
Publication Status
Published
Article Number
ARTN 338