Extended Data Fig. 3: MESA’s ecological framework demonstrates enhanced performance in tissue state characterization compared to existing spatial analysis methods.

(A) Comparison of MESA with other neighborhood identification methods in the mouse spleen CODEX data. Left panel: MESA-derived diversity heatmap showing spatial heterogeneity (red: high diversity; blue: low diversity) with the identified diversity hot spots (dark red) and cold spots (dark blue). Right panel: Cellular neighborhood/niche identified by CellCharter¹⁶, Spatial-LDA¹⁴ and UTAG¹⁴ in the same tissue sample. Distinct colors denote different neighborhoods. MESA-identified hot spots and cold spots reveal diversity patterns not captured by conventional neighborhood/niche methodologies. (B) Comparative analysis of MESA’s ecological metrics versus CellCharter’s metrics in mouse spleen CODEX data of healthy (n = 3) and MRL/lpr (n = 36) samples. The CellCharter’s metrics include a set of quantitative measures to characterize spatial patterns: (1) curl, which quantifies the degree of curvature or twisting in a shape; (2) elongation, measured as the proportion between the longest and shortest axes; (3) linearity, which evaluates how closely a shape follows a straight path; and (4) purity, assessing the homogeneity of cell types within a defined cluster region. Standard box plot metrics were used, with points representing individual tissue samples. Two-sided Welch’s t-test is used to compute the P values, which have been adjusted using the BH procedure for FDR correction. The plot shows only valid CellCharter results for cell types and shape metrics (linearity, curl, elongation, and purity), excluding cases where default settings produced null values.