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
• Keywords: Robustness; Identification; Code (set theory)

The identification of biologically meaningful domains is a central step in the analysis spatial transcriptomic data. Following Occam's razor, we show that simple PCA-based algorithm for unsupervised domain rivals performance ten competing state-of-the-art methods across six single-cell datasets. Our reductionist approach, NichePCA, provides researchers with intuitive interpretation and excels execution speed, robustness, scalability. code available at https://github.com/imsb-uke/nichepca. Supplementary data are Bioinformatics online.