Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 200438 Shanghai, China.
Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 201203 Hangzhou, Zhejiang, China.
J Proteome Res. 2021 Jan 1;20(1):1079-1086. doi: 10.1021/acs.jproteome.0c00488. Epub 2020 Dec 18.
Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer, we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualization of batch effects. We demonstrate its applications in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfor.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.
批次效应是指可能掩盖生物信号的不需要的数据变化,导致后续数据分析出现偏差或错误。因此,对组间效应进行有效的评估和消除对于组学数据分析是必要的。为了方便组间效应的评估和校正,我们在这里展示了 BatchSever,这是一个基于 R/Shiny 的开源、用户友好的交互式图形化网络平台,用于组间效应分析。在 BatchSever 中,我们引入了 autoComBat,这是 ComBat 的一个修改版本,ComBat 是最广泛使用的组间效应校正工具。BatchSever 使用 PVCA(主方差成分分析)和 UMAP(流形逼近和投影)来评估和可视化组间效应。我们在多个蛋白质组学和转录组学数据集上展示了其应用。BatchServer 可在 https://lifeinfor.shinyapps.io/batchserver/ 作为网络服务器使用。源代码可在 https://github.com/guomics-lab/batch_server 上免费获取。