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Updated: May 5, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
PCA-based spatial domain identification with state-of-the-art performance
Darius P Schaub1,2, Behnam Yousefi1,3, Nico Kaiser1,2
1Institute of Medical Systems Bioinformatics, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany.
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.
