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Updated: May 16, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Published on: September 5, 2025

Logistic regression for estimating functional effects with spatial transcriptomics.

Michael Barkasi1, Cody Nhan Pham1, Demetrios Neophytou1,2

  • 1Department of Neuroscience, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave., 63110 Missouri, United States.

Nucleic Acids Research
|May 15, 2026
PubMed
Summary

A new warped sigmoidal Poisson-process mixed-effects (WSP) model enables hypothesis testing for spatial transcriptomics (ST) data. This tool quantifies effects on gene expression spatial distribution without manual preprocessing.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) offers insights into gene function by analyzing gene expression across tissues.
  • Current ST analysis tools primarily focus on data exploration, lacking robust methods for hypothesis testing.
  • There is a need for tools to assess how factors influence the spatial distribution of gene expression.

Purpose of the Study:

  • To introduce a novel statistical model for hypothesis testing in spatial transcriptomics.
  • To provide a method for testing effects on functionally relevant parameters of gene spatial distribution.
  • To enable rigorous quantification and testing of spatial variation in transcriptomic data.

Main Methods:

  • Development of the warped sigmoidal Poisson-process mixed-effects (WSP) model.
  • Alignment of spatial coordinates to a specific axis of interest.
  • Application of likelihood-based regression to identify between-group effects on expression rates and boundaries.

Main Results:

  • Demonstration of the statistical validity of WSP models using semi-synthetic simulated data.
  • Application of WSP models to MERFISH data from mouse somatosensory cortex.
  • Validation using bulk sequencing data from mouse liver lobules with extrapolated spatial coordinates.

Conclusions:

  • WSP models provide a practical and statistically rigorous approach for ST data analysis.
  • The tool facilitates hypothesis testing without the need for error-prone manual preprocessing.
  • WSP models enable minimally biased testing of effects on spatial variation in gene expression.