Prusina, T. | Search bioRxiv

bioRxiv2026

An explanatory benchmark of spatial domain detection reveals key drivers of method performance

The spatial organization of cells within tissues is critical for understanding biological function and disease, and spatial transcriptomics enables genome-wide mapping of this organization. Numerous computational methods aim to identify spatial domains, yet their performance is often evaluated on limited datasets, leading to conflicting conclusions. Here, we present an explanatory benchmark of 26 spatial domain detection methods across 63 tissue sections from six spatial transcriptomics technologies, supplemented by over 1,000 semi-synthetic datasets that systematically vary resolution, gene panel size, and tissue architecture. By jointly analyzing real and semi-synthetic data across this broad parameter space, our benchmark uncovers systematic performance differences and sources of variability that are obscured in standard evaluations. Although most spatial methods outperform non-spatial baselines, their performance depends strongly on data resolution and cellular heterogeneity. To enable systematic analysis beyond individual methods, we introduce a modular, plug-and-play benchmarking framework that facilitates method refinement and component exchange. Using this framework, an ablation study of neural network-based approaches shows that the choice of preprocessing and clustering often has a larger impact on performance than ar-chitectural novelty alone. Together, these results provide a principled foundation for informed method selection and offer guidance for the development of robust and scalable spatial domain detection tools as spatial transcriptomics technologies continue to advance.

bioinformatics↗

bioRxiv2021

Metric Multidimensional Scaling for Large Single-Cell Data Sets using Neural Networks

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a neural network based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

bioinformatics↗