Package: spatialDESeq 0.99.2

spatialDESeq: Spatial Differential Expression via Spatial Autoregressive GLMs

A robust, parallelized statistical framework optimized for identifying spatially differential expression in high-throughput spatial transcriptomics datasets. The package extends standard negative binomial generalized linear models (GLMs) by incorporating continuous physical tissue spot coordinates as distance-weighted local smoothing covariates. This configuration models spatial context seamlessly without relying on pre-annotated categorical tissue clusters. The underlying distance matrix evaluations and coordinate smoothing routines are accelerated natively via an optimized C++ Armadillo matrix engine to guarantee computational efficiency. Built to integrate natively into the Bioconductor ecosystem, the package operates directly on SpatialExperiment containers, incorporates robust library-size normalization scaling factor offsets, calculates adjusted p-values for multi-gene profiles, and supplies an interactive Shiny-based point-and-click visualization dashboard tool for downstream exploratory data analysis.

Authors:Sahal Paladan [aut, cre]