文献类型：期刊文献

英文题名：NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

作者：Yang, Qingxia[1,2];Wang, Yunxia[1];Zhang, Ying[1];Li, Fengcheng[1];Xia, Weiqi[1];Zhou, Ying[3,4];Qiu, Yunqing[3,4];Li, Honglin[5];Zhu, Feng[1,2]

机构：[1]Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou 310058, Peoples R China;[2]Chongqing Univ, Sch Pharmaceut Sci, Chongqing 401331, Peoples R China;[3]Zhejiang Univ, Zhejiang Prov Key Lab Drug Clin Res & Evaluat, Hangzhou 310000, Peoples R China;[4]Zhejiang Univ, Affiliated Hosp 1, Hangzhou 310000, Peoples R China;[5]East China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China

年份：2020

卷号：48

期号：W1

起止页码：W436

外文期刊名：NUCLEIC ACIDS RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000562474100058)】；

基金：National Key Research and Development Program of China [2018YFC0910500]; National Natural Science Foundation of China [81872798, U1909208]; Fundamental Research Funds for Central University [2018QNA7023, 10611CDJXZ238826, 2018CDQYSG0007, CD-JZR14468801]; Key R&D Program of Zhejiang Province [2020C03010]; Leading Talent of `ChinaTen Thousand Plan' -National High-Level Talents Special Support Plan. Funding for open access charge: National Natural Science Foundation of China [81872798].

语种：英文

摘要：Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.