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Logistic regression for estimating functional effects with spatial transcriptomics

ABSTRACT: Spatial transcriptomics (ST) unlocks new potential for studying gene functions in biological processes which depend on the orchestration of transcription across space. However, despite their growing number, analysis tools for ST remain largely aimed at data exploration, with few resources for theory-driven hypothesis testing. What's missing is a way to test whether a factor of interest affects functionally relevant parameters of a gene's spatial distribution. We present a tool to fill this gap, which we call a warped sigmoidal Poisson-process mixed-effects (WSP, pronounced "wisp") model. WSP models are the first ST tool allowing researchers to test biologically critical questions without bespoke preprocessing pipelines for identifying key spatial parameters. By aligning coordinates to an axis of interest and letting a likelihood-based regression find between-group effects on expression rates and boundaries, WSP models replace error-prone manual preprocessing with minimally biased hypothesis testing. WSP models are implemented by wispack ("wisp package"), an R package written in Rcpp. After introducing WSP models, we demonstrate the statistical validity of wispack using semi-synthetic simulated data and demonstrate its ability to test for effects by applying it to MERFISH data from mouse somatosensory cortex and bulk sequencing data from mouse liver lobules with extrapolated spatial coordinates.

ORGANISM(S): synthetic construct Mus musculus

PROVIDER: GSE319949 | GEO | 2026/02/18

REPOSITORIES: GEO

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