Fig. 1: Spatial analysis algorithms implemented in stLearn. | Nature Communications

Fig. 1: Spatial analysis algorithms implemented in stLearn.

From: Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Fig. 1

a Schematic diagram showing the three spatial data types that can be integrated by stLearn: gene expression (G), imaging (I) and spatial distance (D). b stLearn can be applied to a range of spatial technologies, with or without tissue imaging information (using f(G, I, D) or f(G, D) functions). c Spatial trajectory analysis to infer biological processes within an undissociated tissue. Pseudo-space-time distance (PSTD) values are calculated based on gene expression and physical distance. Spatial distance is calculated between the centroid coordinates of clusters U and V with sub-clusters (u1, u2) and (v1, v2, v3). PSTD values are used to construct a rooted, directed graph (arborescence), the topology of which can be optimised by a minimum spanning tree to infer the trajectory. This approach to trajectory analysis was validated in a mouse model of traumatic brain injury. d Spatially-constrained two-level permutation (SCTP) analysis for cell–cell interaction (CCI) between (straight arrows) and within (looped arrows) spatial spots. SCTP uses ligand and receptor co-expression information among neighbouring spots, and cell type diversity (gradient blue spots; darker colour indicates more cell types per spot) to compute ligand-receptor (LR) scores. SCTP finds hotspots (purple) within a given tissue, where LR interactions between cell types are more likely to occur compared to a null distribution of random non-interacting gene-gene pairs. Predicted interactions were confirmed by RNA single molecule imaging. e Overview of within-tissue imputation and clustering by stSME, which corrects for technical noise (dropouts) in gene expression values by using imaging data (via a neural network model - matrix I), and spots that are both physically near and have similar gene expression profiles (distance matrices D and G, respectively). stSME can also predict gene expression in tissue regions for which there is no experimental data (pseudo-spots). stSME clustering performance was validated against an established anatomical reference mouse brain (spatial brain data, top far right), or expert pathologist annotation (breast cancer data, bottom far right).

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