ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data
- 1Johns Hopkins Bloomberg School of Public Health
Abstract
The rapid adoption of spatial transcriptomics (ST) technologies has been accompanied by substantial growth in computational methods, creating an increasing need for systematic and reliable benchmarking, particularly as analyses increasingly extend from individual tissue sections to three-dimensional (3D) tissue reconstruction. Existing benchmarks often rely on a small and similar set of experimentally-derived datasets because they offer a reliable ground truth label, which is hard to find in practice. Simulated or synthetic data provide an alternative, yet most existing simulators either adapt single-cell RNA-sequencing models to spatial coordinates or generate individual sections in a two-dimensional (2D) space at predefined resolutions. More recent approaches can simulate 2D and 3D tissues, but generally do not derive multiple spatial resolutions from the same molecular realization. Here, we introduce ALBIS (A muLti-resolution Biological In-silico Simulator for spatial transcriptomics), a statistically grounded and highly customizable framework that generates synthetic ST data from an in silico 3D tissue. ALBIS defines spatially structured cell populations with corresponding markers for gene expression, then initializes individual mRNA molecules in 3D space to create cell-level information with a ground truth 3D coordinate for each molecule and cell. From this common realization, ALBIS cuts the 3D tissue into multiple 2D tissue sections with cell-, bin-, and spot-level measurements, enabling direct comparison across spatial resolutions. Properties of the simulated data, including spatial domains, cell type composition, gene-expression effects, batch effects, measurement resolution, and tissue scale, can be systematically varied, enabling rigorous stress-testing of computational methods under known and increasingly challenging conditions.
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BibTeX
@misc{matthias2026albis,
title = {{ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data}},
author = {J. H. Matthias and J. Yao and S. C. Hicks},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.757026},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.757026v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data
AU - J. H. Matthias
AU - J. Yao
AU - S. C. Hicks
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.757026
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.757026v1
AB - The rapid adoption of spatial transcriptomics (ST) technologies has been accompanied by substantial growth in computational methods, creating an increasing need for systematic and reliable benchmarking, particularly as analyses increasingly extend from individual tissue sections to three-dimensional (3D) tissue reconstruction. Existing benchmarks often rely on a small and similar set of experimentally-derived datasets because they offer a reliable ground truth label, which is hard to find in practice. Simulated or synthetic data provide an alternative, yet most existing simulators either adapt single-cell RNA-sequencing models to spatial coordinates or generate individual sections in a two-dimensional (2D) space at predefined resolutions. More recent approaches can simulate 2D and 3D tissues, but generally do not derive multiple spatial resolutions from the same molecular realization. Here, we introduce ALBIS (A muLti-resolution Biological In-silico Simulator for spatial transcriptomics), a statistically grounded and highly customizable framework that generates synthetic ST data from an in silico 3D tissue. ALBIS defines spatially structured cell populations with corresponding markers for gene expression, then initializes individual mRNA molecules in 3D space to create cell-level information with a ground truth 3D coordinate for each molecule and cell. From this common realization, ALBIS cuts the 3D tissue into multiple 2D tissue sections with cell-, bin-, and spot-level measurements, enabling direct comparison across spatial resolutions. Properties of the simulated data, including spatial domains, cell type composition, gene-expression effects, batch effects, measurement resolution, and tissue scale, can be systematically varied, enabling rigorous stress-testing of computational methods under known and increasingly challenging conditions.
ER -