Biology subjects

Luecken, M.

Publications and source records attributed to Luecken, M..

3 recordsLinked to original sources

bioRxiv2025

Beyond Visual Inspection: Principled Benchmarking of Single-Cell Trajectory Representations with scTRAM

Single-cell representation learning aims to construct low-dimensional embeddings that preserve biologically salient manifold structures. Yet current evaluation protocols remain inadequate for assessing trajectory fidelity--essential for studying cell differentiation, immune responses, disease progression and perturbation--relying on heuristic visualizations or scalar surrogates that overlook multiscale trajectory geometry. To address this gap, we present scTRAM--single-cell trajectory representation metrics--a principled benchmarking framework that quantitatively evaluates how well single-cell embeddings retain groundtruth trajectories across complementary failure modes, from local neighborhood scrambling to global branch mis-ordering. Through our experiments, we show that scTRAM reveals modelspecific trade-offs in preserving distinct aspects of trajectory structure, with edge-specific decomposition exposing localized representational biases at high resolution. Systematic validation across diverse biological contexts confirms that the metric suite is sensitive to genuine structural properties rather than statistical artifacts. By shifting from qualitative, ad-hoc judgments to quantitative, principled approach, scTRAM enables rigorous comparison of embedding methods and informs downstream analyses where trajectory integrity is essential for biological interpretation.

bioinformatics↗

bioRxiv2025

geneRNIB: a living benchmark for gene regulatory network inference

Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health and disease. GRN inference has received substantial attention, motivating systematic benchmarking. Despite various benchmarking efforts, existing studies remain limited in the number of methods, datasets, and metrics, fail to capture the context-specific nature of regulatory interactions across biological conditions, and are constrained by the absence of a reliable ground truth. Here, we introduce geneRNIB, a comprehensive GRN inference benchmarking framework built on three key principles: continuous integration, context-specific evaluation, and holistic assessment in the absence of a true reference network. geneRNIB enables the seamless incorporation of new algorithms, datasets, and evaluation metrics to reflect ongoing developments. In the current version, we systematically integrated and assessed 12 GRN inference methods, spanning single- and multiomics approaches across 11 datasets including thousands of perturbation scenarios. We introduced complementary metrics specifically designed to assess context-specific inference. Our findings indicate that simple models with fewer assumptions often outperform more complex pipelines across several perturbation-informed and predictive metrics. Notably, gene expression-based algorithms yielded better results than more advanced multimodal approaches. In addition, we identify several potential factors that influence the performance of GRN inference and offer actionable guidelines for the future development of the method. By addressing these critical limitations in existing benchmarks, geneRNIB advances GRN inference research and fosters progress toward personalized medicine.

bioinformatics↗

bioRxiv2020

Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

systems biology↗