PCA-based spatial domain identification with state-of-the-art performance.
Where this comes from
- Record sourced from PubMed, PMID 39775801.
- Also identified by DOI 10.1093/bioinformatics/btaf005 and PMC identifier 11761416.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data. 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. The code is available at https://github.com/imsb-uke/nichepca.
Medical subject headings
- Algorithms
- Principal Component Analysis
- Transcriptome
- Gene Expression Profiling
- Single-Cell Analysis
- Computational Biology