Robust volcano plot: identification of differential metabolites in the presence of outliers.

ABSTRACT:

Background

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.

Results

We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower miscl

SUBMITTER: Kumar N 

PROVIDER: S-EPMC5896081 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature