A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.

ABSTRACT: Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabol

SUBMITTER: Yang Q 

PROVIDER: S-EPMC7711263 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature