Mass spectrometry (MS)-based metabolomics has emerged as a powerful tool to address multifaceted biological questions. Commercial solutions like the ones developed at biocrates allow reliable and quantitative targeted metabolic profiling, including the conversion of the raw MS spectra into absolute concentrations of metabolites. These results can be exported for further analysis under several formats with varying levels of human- vs. machine-readability. The default output format is an Excel spreadsheet that favours human readability and therefore requires extra preparation steps for downstream bioinformatic analysis and data exploration. To streamline this next step for users of this platform, we developed MetAlyzer, an R package (https://github.com/Lu-Group-UKHD/MetAlyzer) specifically designed to handle the spreadsheets generated by WebIDQ, the biocrates workflow manager software. MetAlyzer converts WebIDQ-generated spreadsheets into flexible SummarizedExperiment objects and provides functions for data preprocessing, statistical testing, and visualization of differential metabolites. To further support data exploration and hypothesis generation by users without coding experience, we also developed an interactive and intuitive Shiny app (https://metalyzer.shinyapps.io/MetAlyzer_ShinyApp/) that interfaces with MetAlyzer’s core functionality, enabling users to execute the complete analysis workflow without writing code. This combination can help scientists deepen their understanding of metabolomics results, supporting the broader adoption of metabolomics in the life sciences community.
Magnetoencephalography reveals adaptive neural reorganization maintaining lexical-semantic proficiency in healthy aging
Although semantic cognition remains behaviorally stable with age, neuroimaging studies report age-related alterations in response to semantic context. We aimed to reconcile these inconsistent findings