Data_Sheet_1_Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction.ZIP

MotivationTensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.ResultsWe developed two bioconductor packages—TDbasedUFE and TDbasedUFEadv—that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.Availability and implementationTDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

基于张量分解（Tensor Decomposition，简称TD）的无监督特征提取（Unsupervised Feature Extraction，简称UFE）在生物信息学领域得到了广泛应用，其应用范围涵盖了从生物标志物识别到致病因基因鉴定以及药物重新定位等多个方面。然而，由于缺乏面向非专家用户友好的工具，基于TD的无监督特征提取未能得到广泛认可。本研究开发了两个生物信息学工具包——TDbasedUFE和TDbasedUFEadv，使不熟悉TD的科研人员能够利用TD进行无监督特征提取。这两个工具包简化了差异表达基因的识别和多组学分析。研究发现，TDbasedUFE在性能上优于DESeq2和DIABLO等两种最先进的方法。工具包的获取与实现TDbasedUFE和TDbasedUFEadv作为R/Bioconductor包免费提供，可通过以下链接获取：https://bioconductor.org/packages/TDbasedUFE和https://bioconductor.org/packages/TDbasedUFEadv。