论文标题
Flimma：用于差异基因表达分析的联合和隐私的工具
Flimma: a federated and privacy-preserving tool for differential gene expression analysis
论文作者
论文摘要
跨医院的转录组数据可以提高差异表达分析的灵敏度和鲁棒性，从而产生更深层次的临床见解。由于数据交换通常受到隐私立法的限制，因此经常使用荟萃分析来汇集本地结果。但是，如果类标签不均匀地分布在队列之间，则其准确性可能会下降。 Flimma（https://exbio.wzw.tum.de/flimma/）通过以隐私性的方式实施state-of-the-the-the-art Workflow limma voom来解决此问题，即患者数据永远不会离开其源站点。 FLIMMA结果与Limma Voom在合并数据集中生成的结果相同，即使在荟萃分析方法失败的情况下，也是在混合数据集上产生的结果。
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 (https://exbio.wzw.tum.de/flimma/) 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.
