NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.
Qingxia Yang,Qingxia Yang,Yunxia Wang,Ying Zhang,Fengcheng Li,Weiqi Xia,Ying Zhou,Yunqing Qiu,Honglin Li,Feng Zhu,Feng Zhu +10 more
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TLDR
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.Abstract:
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/.read more
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References
More filters
Journal ArticleDOI
R: A Language for Data Analysis and Graphics
Ross Ihaka,Robert Gentleman +1 more
TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.
Journal ArticleDOI
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
TL;DR: Three methods of performing normalization at the probe intensity level are presented: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure and the simplest and quickest complete data method is found to perform favorably.
Journal ArticleDOI
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.
Yixin Wang,Jan Klijn,Yi Zhang,Anieta M. Sieuwerts,Maxime P. Look,Fei Yang,Dmitri Talantov,Mieke Timmermans,Marion E. Meijer-van Gelder,Jack X. Yu,Tim Jatkoe,Els M.J.J. Berns,David Atkins,John A. Foekens +13 more
TL;DR: The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.
Journal ArticleDOI
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.
Jasmine Chong,Othman Soufan,Carin Li,Iurie Caraus,Shuzhao Li,Guillaume Bourque,David S. Wishart,Jianguo Xia +7 more
TL;DR: The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions.
Journal ArticleDOI
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber,Vincent J. Carey,Robert Gentleman,Simon Anders,Marc R. J. Carlson,Benilton S. Carvalho,Héctor Corrada Bravo,Sean Davis,Laurent Gatto,Thomas Girke,Raphael Gottardo,Florian Hahne,Kasper D. Hansen,Rafael A. Irizarry,Michael S. Lawrence,Michael I. Love,James W. MacDonald,Valerie Obenchain,Andrzej K. Oleś,Hervé Pagès,Alejandro Reyes,Paul Shannon,Gordon K. Smyth,Dan Tenenbaum,Levi Waldron,Martin Morgan +25 more
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.