Niklas Müller‐Bötticher

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

The paper extends SpatialLeiden to handle atlas-scale, 3D, and multimodal spatial omics data, improving modularity and scalability across multiple samples.

This paper introduces a tool to detect overlapping cells and segmentation errors in 3D spatial transcriptomics data.

SpatialLeiden integrates spatial embeddings into Leiden clustering to create a spatially aware method that performs well compared to existing spatial clustering techniques.

Sainsc provides a segmentation-free tool for analyzing high-resolution spatial transcriptomics data, enabling accurate subcellular cell-type mapping with lower computational demands.

SSAM-lite provides a lightweight, web-based tool for rapid, segmentation-free analysis of spatially resolved transcriptomics data, enabling broad accessibility without requiring computational expertise.