Biology subjects

Herfurth, L.

Publications and source records attributed to Herfurth, L..

2 recordsLinked to original sources

bioRxiv2025

MetAlyzer to Perform Streamlined, Interactive, and Pathway-Mapping Analysis of Targeted Metabolomics Data from the biocrates Platform

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 MetAlyzers 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.

bioinformatics↗

bioRxiv2025

Tracking biological hallucinations in single-cell perturbation predictions using scArchon, a comprehensive benchmarking platform

The accurate prediction of cellular responses to perturbations, such as drug treatments, remains a pivotal challenge in single-cell transcriptomics. While numerous deep learning tools have been developed for this task, their systematic benchmarking across diverse datasets and performance metrics has been limited. Here, we present scArchon, a reproducible, modular benchmarking platform built on Snakemake, designed to evaluate perturbation response prediction tools in an unbiased and extensible manner. Employing four representative single-cell RNA-seq datasets, we compare leading methods such as scGen, CPA, trVAE, scPRAM, scVIDR, scDisInFact, SCREEN, scPreGAN, and CellOT against baselines. We assess model performance using a composite of statistical and biological metrics. Our analysis reveals heterogeneous performance: while methods like trVAE, scGen, scPRAM, and scVIDR achieve robust results across multiple datasets, other tools occasionally underperform even compared to linear or control baselines. Notably, models with favorable quantitative scores may fail to retain key biological perturbation signatures, underscoring the need for gene-level evaluation. scArchon provides a unified, extensible foundation for large-scale, standardized benchmarking of perturbation prediction tools, facilitating methodological transparency and accelerating development in this rapidly evolving field. We encourage adoption of scArchon and sharing of containerized tools to drive progress in single-cell perturbation modeling.

bioinformatics↗