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

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.