Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.
Genome Biol. 2021 Dec 14;22(1):338. doi: 10.1186/s13059-021-02553-2.
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.
跨医院汇总转录组学数据可以提高差异表达分析的灵敏度和稳健性，从而获得更深入的临床见解。由于数据交换通常受到隐私法规的限制，因此经常采用荟萃分析来汇总局部结果。但是，如果类标签在队列之间不均匀分布，则准确性可能会下降。Flimma（https://exbio.wzw.tum.de/flimma/）通过以联邦方式实现最先进的 limma voom 工作流程来解决此问题，即患者数据从未离开其源站点。即使在荟萃分析方法失败的不平衡情况下，Flimma 的结果与在汇总数据上生成的 limma voom 结果完全相同。
