Fig. 1: nnSVG recovers biologically informative SVGs with gene-specific length scale parameters.

Using the Visium human DLPFC dataset⁸, nnSVG, SPARK-X, HVGs, and Moran’s I were applied to identify SVGs. a Spatial expression plots of 6 known biologically informative SVGs, including cortical layer-associated SVGs (top row) and blood- and immune-associated SVGs (bottom row). b Distribution of estimated gene-specific length scale parameters from nnSVG, with the 6 SVGs from (a) labeled in red. The blood- and immune-associated SVGs have smaller estimated length scale parameters than the cortical layer-associated SVGs. c Rank order of the 6 SVGs from (a) within the lists of top SVGs. Dashed vertical line divides the genes into the 3 cortical layer-associated SVGs with large length scales (left) and the 3 blood- and immune-associated SVGs with small length scales (right). d Estimated likelihood ratio (LR) statistic from nnSVG (y-axis) compared to the rank per gene (x-axis), with the 6 SVGs from (a) labeled, and 134 additional known layer-specific marker genes (from manually guided analyses by Maynard et al.⁸) highlighted (red circles). Orange dashed vertical line indicates rank cutoff for statistically significant SVGs at a multiple-testing-adjusted p-value of 0.05 using LR test with 2 degrees of freedom. e Estimated effect size (proportion of spatial variance) along y-axis compared to the mean log-transformed normalized counts (logcounts) along x-axis for top 1000 SVGs from nnSVG, with the 6 SVGs from (a) labeled, and estimated LR statistic per gene indicated with color scale. f Ranks of top 1000 SVGs from nnSVG (y-axis) compared to ranks from baseline methods (x-axis) using HVGs (nonspatial baseline method, left) and Moran’s I (spatially-aware baseline method, right), with SVGs from (a) highlighted (black circles), and Spearman correlation (text labels).