Logistic regression for estimating functional effects with spatial transcriptomics.

Overview

1. Department of Neuroscience, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave., 63110 Missouri, United States
2. Department of Biology, The City University of New York Graduate Center, 365 Fifth Ave., 10016 New York, United States

Abstract

Spatial transcriptomics (ST) unlocks potential for studying gene functions in processes that depend on orchestration of transcription across space. However, analysis tools for ST remain aimed at data exploration, with few resources for 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 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. After introducing WSP models, we demonstrate their statistical validity using semi-synthetic simulated data and their ability to test for effects by applying them to MERFISH data from mouse somatosensory cortex and bulk sequencing data from mouse liver lobules with extrapolated spatial coordinates. Together, these validations and applications demonstrate that WSP models offer a practical and statistically rigorous approach to quantifying and testing for effects on spatial variation in transcriptomic data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

• alleninstitute.github.io/abc_atlas_access — at alleninstitute.github.io; found in “Data availability”
• geo:GSE319949 — at NCBI GEO; found in “Data availability”
• github.com/naef-lab/circadian-zonation — at github.com; found in “Data availability”

Other data links

• ncbi.nlm.nih.gov/geo — NCBI; found in “Data availability”

Data availability

S1 MERFISH data: Raw image files are available on the European Bioinformatics Institute’s (EBI) BioImage Archive at ebi.ac.uk/bioimage-archive/, accession number S-BIAD2957, DOI: 10.6019/S-BIAD2957. Vizgen Post-processing Tool output with CCFv3 registration is available as HDF5 files on the NIH Gene Expression Omnibus (GEO) atncbi.nlm.nih.gov/geo/, accession number GSE319949 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319949). Liver–lobule data from Droin et al. [14] is available at github.com/naef-lab/Circadian-zonation/tree/master/Datasets/Profiles. MERFISH data from Yao et al. [55] is available at alleninstitute.github.io/abc_atlas_access/descriptions/MERFISH-C57BL6J-638850.html. Available at github.com/Oviedo-Lab/wspmm_methods and https://doi.org/10.5281/zenodo.19681335 are the following files:

S1_laminar_countdata_NARresub_feb14_2026 .csv: final version of the S1 MERFISH data with transformation into laminar coordinates.

Droin_radial_count_data_sim.csv: preprocessed version of the Droin et al. [14] data used here.

Allen_data.csv: seed data used for attractor simulations.

benchmark_results_NARresub_feb14_2026 .csv: results of running the 250 attractor-simulation benchmarking.

This Git Repo also contains all the code used in this paper, excluding wispack.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 MeSH terms, 1 funder, 77 references, 1 RRID.

Cite

This paper

Barkasi, M., Pham, C. N., Neophytou, D., & Oviedo, H. V. (2026). Logistic regression for estimating functional effects with spatial transcriptomics. Nucleic acids research, 54(9), gkag466. https://doi.org/10.1093/nar/gkag466

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