Fig. 2: nnSVG recovers biologically informative SVGs within spatial domains. | Nature Communications

Fig. 2: nnSVG recovers biologically informative SVGs within spatial domains.

From: nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Fig. 2: nnSVG recovers biologically informative SVGs within spatial domains.The alternative text for this image may have been generated using AI.

Using the Slide-seqV2 mouse hippocampus (HPC) dataset4, nnSVG, SPARK-X, HVGs, and Moran’s I were applied to identify SVGs within an a priori defined spatial domain. a Computationally labeled cell types per spot (bead) with labels from ref. 39. b Spatial expression plots of 2 known biologically informative SVGs identified by Cable et al.39 showing spatial gradients of expression within the spatial domain defined by CA3 cell type labels (pink points in (a)). c Rank order of the 2 SVGs from (b) within the lists of top SVGs. d Estimated likelihood ratio (LR) statistic from nnSVG (y-axis) compared to the rank per gene (x-axis), with the 2 SVGs from (b) highlighted. 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.

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