Atlas-scale spatially aware clustering with support for 3D and multimodal data using SpatialLeiden

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Here we extend SpatialLeiden, our spatial clustering algorithm, to enable generalised atlas-scale multi-sample, 3D serial-section, and multimodal spatial omics via flexible neighbour-graph multiplexing on batch-corrected latent spaces. It delivers coherent domains aligning with brain atlases across >100 samples, stable 3D reconstruction of cancer tissue structures, and integrated multimodal features, outperforming specialized tools in modularity and scalability on standard hardware. SpatialLeiden is compatible with scverse for broad and intuitive adoption.

Authors 6

1. Niklas Müller‐Bötticher

   Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Berlin Institue of Health Charité;

2. Alexander Malt

   Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Berlin Institute of Health Charité;

3. Paul Kießling Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Roland Eils

   Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Berlin Institute of Health Charité;

5. Christoph Kuppe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

6. Naveed Ishaque corresponding

   Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Berlin Institute of Health Charité;