Spatial transcriptomics is currently revolutionizing discovery across biology and medicine, particularly in neuroscience and oncology. It is now possible to determine which genes are (co-) expressed in specific regions or cell types, where they are located in tissue, and how neighboring cells relate. However, extracting this information is computationally demanding, whether using chip-based platforms for genome-wide coverage or microscopy-based systems for high spatial resolution. A common compromise is to preselect genes of interest, known as spatially variable genes (SVGs).
We present Image Correlation of Expression ProfileS (ICEPS), a new method for SVG detection based on spatial autocorrelation length. ICEPS compares favorably with existing approaches: it is more sensitive to compact, spatially extended expression patterns than methods focused only on spot-to-spot variance, and more robust to random single-spot expression spikes, likely technical artifacts, than the widely used Moran's I statistic. Although some more sophisticated methods, such as nnSVG, may outperform ICEPS in certain settings, ICEPS is substantially faster because it relies on the classic FFT algorithm. We recently extended ICEPS with pairwise cross-correlation (BICEPS) and additional correlation-based features that distinguish different spatial pattern classes.
Auto- and cross-correlation techniques are widely used and well-understood tools in classical image analysis. With its favorable characteristics compared to several current tools, ICEPS and BICEPS should find their niche in the spatial transcriptomics toolbox.
[1] Weber, L.M., et al. Nat Commun 14, 4059 (2023).

Co-authors: None