Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region.