Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium.
Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Universiteit Hasselt, Agoralaan, Diepenbeek 3590, Belgium.
J Proteome Res. 2021 Apr 2;20(4):2151-2156. doi: 10.1021/acs.jproteome.0c00977. Epub 2021 Mar 11.
For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.
对于所有组学领域的差异表达研究，数据标准化是一个关键步骤，通常需要在速度和有效性之间进行平衡。为了跟上高通量仪器产生的数据，研究人员需要快速、易于使用且有效的方法，这些方法适合自动化分析管道。CONSTANd 标准化方法符合这些标准，因此我们为 R/BioConductor 和 Python 提供了它的源代码。我们简要回顾了该方法，并展示了如何在不同的组学环境中针对任何规模的实验使用它。在组学领域的广泛采用将简化多组学实验中的数据整合。
