Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics

Large-scale and long-period metabolomics
study is more susceptible
to various sources of systematic errors, resulting in nonreproducibility
and poor data quality. A reliable and robust batch correction method
removes unwanted systematic variations and improves the statistical
power of metabolomics data, which undeniably becomes an important
issue for the quality control of metabolomics. This study proposed
a novel data normalization and integration method, Norm ISWSVR. It
is a two-step approach via combining the best-performance internal
standard correction with support vector regression normalization,
comprehensively removing the systematic and random errors and matrix
effects. This method was investigated in three untargeted lipidomics
or metabolomics datasets, and the performance was further evaluated
systematically in comparison with that of 11 other normalization methods.
As a result, Norm ISWSVR decreased the data’s median cross-validated
relative standard deviation (cvRSD), increased the correlation between
QCs, improved the classification accuracy of biomarkers, and was well-compatible
with quantitative data. More importantly, Norm ISWSVR also allows
a low frequency of QCs, which could significantly decrease the burden
of a large-scale experiment. Correspondingly, Norm ISWSVR favorably
improves the data quality of large-scale metabolomics data.