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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.

Luis Chumpitaz-Diaz, Priyanka Shrestha, Barbara E Engelhardt

Biorxiv : the Preprint Server for Biology

|July 11, 2026

Summary

This summary is machine-generated.

Scalable multi-group nonnegative spatial factorization (smNSF) integrates spatial and cell-type data to reveal hidden gene expression patterns. This method disentangles cell-type specific spatial programs, enhancing biological interpretability in spatial transcriptomics.

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Published on: November 7, 2025

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) technologies offer insights into tissue structure and disease by analyzing gene expression in spatial context.
Current ST analysis methods often fail to separate spatial gene patterns from cell-type differences, limiting biological interpretability.
Existing methods overlook spatial information or struggle to distinguish spatial gene patterns from cell-type driven differences.

Purpose of the Study:

To develop a computationally tractable probabilistic framework that integrates spatial coordinates and cell-type labels for ST data analysis.
To address the challenge of convolving gene expression patterns with cell-type proportions in existing dimension reduction methods.
To enable cell-type aware spatial decompositions and facilitate in silico exploration of spatial patterns and cellular identity.

Main Methods:

Introduced scalable multi-group nonnegative spatial factorization (smNSF), a unified matrix factorization model.
Utilized multi-group Gaussian processes (MGGPs) as priors to capture cell-type specific spatial variation with enforced nonnegativity.
Developed a variational inference framework for MGGPs to support scalable optimization and improve numerical stability.

Main Results:

smNSF recovers sparse, interpretable spatial factors across diverse ST datasets.
Cell-type conditional posteriors organize factors into cell-type enriched, specific, and universal spatial programs.
Conditioning on cell types reveals spatial patterns invisible in standard analyses, highlighting cell-type specific contributions to tissue organization.

Conclusions:

smNSF effectively disentangles biological sources of variation in ST data.
The method enables cell-type aware spatial decompositions, enhancing the biological interpretability of spatial gene expression.
smNSF supports in silico exploration of gene expression changes related to tissue and cell-type composition.