The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit\'s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.
In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.
结果:使用以不同表达信号强度为特征的7个数据集测试了每种工具的效率,以捕获广谱的RNA表达数据集。我们的工具包的决策反映了数据集中的稳定分区的实际数量,其中子组是可辨别的。在生物学区别不太明确的数据集中,我们的工具要么形成了具有不同表达谱和可靠临床关联的稳定亚组,要么揭示了有问题数据的迹象,例如偏倚测量.
结论:结论:Omada成功地自动化了转录组数据的健壮无监督聚类,即使对于那些没有广泛的机器学习专业知识的人来说,也能使高级分析变得容易和可靠。Omada的实施可在http://biocorductor.org/packages/omada/上获得。