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Marco Stock

Alumni

Publications

Hidden sampling biases inflate performance in gene regulatory network inference
Marco Stock
Florin Ratajczak
Paul Bertin
Eva Hoermanseder
Yoshua Bengio
Jason Hartford
Pascal Falter-Braun
Matthias Heinig
Alexander Tong
Antonio Scialdone
Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Re… (voir plus)cent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to realistic biological settings. Here, we show that a key reason for that is the way negative regulatory interactions are sampled for supervised training and evaluation. We find that widely used sampling strategies introduce node-degree biases that allow models to exploit trivial graph-structural cues rather than biological signals. Across multiple benchmarks, simple degree-based heuristics match or exceed state-of-the-art graph neural network models under these biased evaluation protocols. We further introduce a degree-aware sampling approach that eliminates these artifacts and provides more reliable assessments of GRN inference methods. Our results call for standardized, bias-aware benchmarking practices to ensure meaningful progress in supervised GRN inference from single-cell RNA-seq data.
2025-12-22
bioRxiv (prépublication)
doi.org
geneRNIB: a living benchmark for gene regulatory network inference
Jalil Nourisa
Antoine Passemiers
Jeremie Kalfon
Marco Stock
Berit Zeller‐Plumhoff
Robrecht Cannoodt
Christian Arnold
Alexander Tong
Jason Hartford
Mihai G. Netea
Antonio Scialdone
Laura Cantini
Yves Moreau
Daniele Raimondi
Yang Eric Li
Malte D. Luecken
Abstract Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health and disease. GRN inference ha… (voir plus)s 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.
2025-02-28
bioRxiv (accepté)
doi.org