Feature annotation in liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics remains challenging. Retention time (RT) prediction can support candidate prioritization and improve annotation confidence. Here, we present FastRet, an R package predicting RTs using Least Absolute Shrinkage and Selection Operator (LASSO) and Boosted Regression Trees (BRT) on molecular descriptors. FastRet provides a flexible framework combining from-scratch model training, selective measuring to prioritize metabolites for remeasurement, and model adjustment to adapt existing models to changed chromatographic conditions. Model training and prediction are completed within seconds on a single CPU core, and FastRet is accessible both from the R console and through a web interface. We validated FastRet on three in-house data sets covering reversed-phase chromatography (RP; N = 458), RP–anion-exchange mixed-mode chromatography (RP-AXMM; N = 436), and hydrophilic interaction chromatography (HILIC; N = 388), plus one external HILIC data set from the Retip package (N = 970). Using a 2:1 training/test split, BRT models trained from scratch achieved a test-set coefficient of determination (R 2) of 0.86, 0.66, and 0.81 for the three in-house data sets. FastRet can also adjust a model to new chromatographic conditions from a few remeasured metabolites: using 25 RP metabolites measured under six modified conditions, adjustment reached R 2 of 0.74 to 0.84 on unseen metabolites, a mean 0.22 gain over from-scratch models. Compared with published methods on identical splits, FastRet showed competitive performance for de novo prediction and superior performance in low-data transfer scenarios, while generalizing to 14 external data sets (median held-out R 2 0.59). FastRet is available on CRAN with the web interface hosted at https://fastret.spang-lab.de.
F. Fadil, T. Schmidt, Christian Amesoeder et al.· Journal of Chemical Informat...· 0 citations
Summary Background Flow cytometry captures subtle changes in immune cell distributions caused by disease, but its full potential in medical decision-making is presently limited by technical variability across instruments, sites and time. To accelerate development of generalisable diagnostic, prognostic or predictive clinical tests, we assembled a benchmark flow cytometry dataset over 20 months using 6 cytometers at 4 independent laboratories in Spain and Germany. Cohorts were amalgamated using a new alignment strategy, CD3-relativisation. Methods Four hundred and eighty-two clinical flow cytometry samples from 381 healthy donors and a further 100 samples from post-surgical patients admitted to intensive care were stained with a 10-colour T cell marker panel. To align data from different cohorts, we introduced CD3-relativisation, a method for normalising fluorescence intensities per-channel against CD3 signals. The quality of data alignment was evaluated using optimal transport distances (OTD), clustering consistency and predictive performance. Findings Our fully annotated data resource (Zenodo 17094078) revealed systematic biases in flow cytometry measurements across time, locations and cytometers. CD3-relativisation minimised these biases without sacrificing biological information. Cell clustering performance and sample-to-sample variability improved after relativisation. Consequently, we were able to predict CMV-IgG serostatus, age and sex with superior precision without relying upon external calibrators, measurement of paired samples, batch definitions or data sharing. Models established in healthy control populations were transferable to a cohort of critically unwell, post-surgical patients. Interpretation Our CD3-relativised benchmark dataset establishes a robust standard for evaluating computational methods in clinical cytometry, especially their stability over time and generalisability between instruments, laboratories and clinically heterogeneous populations. Funding This work was supported by the BMS-Foundation (FA-19-009), BZKF (BF/04/R/Hutch), EU-H2020 (Immutol_101080562, exTra_101119855, PAVE_861190), BMBF (01KD2206I) and DFG (403161218).
Gunther Glehr, K. Kronenberg, Fabiola Arella et al.· EBioMedicine· 0 citations
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