Hajihosseini, M. | Search bioRxiv

bioRxiv2026

An automated platform for spatial functional modeling and fingerprint analysis of tissue molecular landscapes

Spatial transcriptomics (ST) enables high-resolution molecular profiling while preserving tissue architecture, creating new opportunities to investigate how disease-associated pathways are organized within tissues. However, existing analytical approaches largely focus on individual pathways or cell types and do not provide a unified framework for modeling spatially varying pathway interactions across tissue sections and anatomical planes. We present an integrative framework, Spatial Fingerprints Analytics (SFinx), that introduces the concept of a spatial fingerprint for representing patterns of molecular signatures, and Spatial Functional Data Analysis for spatial regression and mapping of localized pathway activity and pathway-phenotype interactions in complex tissues. Applying SFinx to ST datasets on murine lupus nephritis, we reconstructed continuous spatial landscapes of pathway activity and disease-associated phenotypes across kidney sections. This approach identified anatomically restricted inflammatory domains characterized by coordinated activation of immune pathways and revealed substantial spatial heterogeneity in pathway crosstalk across renal compartments. Using generalized additive models with tensor-product splines, we quantified spatially varying associations between lupus nephritis and neutrophil activation pathways across tissue sections, uncovering regions with both positive and negative relationships that would be obscured by conventional bulk analyses. Multi-slice integration further demonstrated reproducible spatial interaction patterns while accounting for section-specific variability. Together, SFinx transforms mixed-spot transcriptomic measurements into interpretable spatial pathway landscapes and interaction maps, providing a general framework for identifying localized disease mechanisms. SFinx revealed previously unrecognized spatial organization of inflammatory signaling in lupus nephritis and presents a broadly applicable strategy for studying spatially coordinated biological processes in cancers, autoimmune and neurodegenerative diseases.

bioinformatics↗

bioRxiv2022

Geographically Weighted Linear Combination Test for Gene Set Analysis of a Continuous Spatial Phenotype as applied to Intratumor Heterogeneity.

BackgroundThe impact of gene-sets on phenotype is not necessarily uniform across different locations of a cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. MethodsAt each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross validation procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. ResultsIn an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. ConclusionsOur proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding contextual heterogeneity of cancer cells.

bioinformatics↗