PCA-based spatial domain identification with state-of-the-art performance

• DOI: 10.1093/bioinformatics/btaf005
• Publication Date: 2025-01-07T18:44:52Z
• Authors: Darius P Schaub; Behnam Yousefi; Nico Kaiser; Robin Khatri; Victor G Puelles; Christian F Krebs; Ulf Panzer; Stefan Bonn

Abstract

Motivation The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.

Results Following Occam’s razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.

Availability and implementation The code is available at https://github.com/imsb-uke/nichepca.