Data-Adaptive Grassmann Manifold Representations for Spatial Transcriptomics Alignment
Abstract
Spatial transcriptomics measures gene expression together with spatial coordinates, but many existing analysis methods represent each spot primarily by a single feature vector. We propose GrassST, a subspace method for spatial transcriptomics analysis and cross-slice alignment. For each spatial spot, GrassST constructs a neighborhood from tissue coordinates, fits a low-dimensional subspace to the embedded expression profiles in that neighborhood, and represents the spot by the resulting subspace. These representations are points on a Grassmann manifold and can be compared using distances between subspaces. GrassST selects the neighborhood size and subspace rank from the spectral energy of the data, allowing these parameters to vary across datasets rather than being fixed globally. Experiments on four spatial transcriptomics datasets show that GrassST achieves competitive clustering and cross-slice integration performance under a unified evaluation pipeline.
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BibTeX
@misc{wang2026dataadaptive,
title = {{Data-Adaptive Grassmann Manifold Representations for Spatial Transcriptomics Alignment}},
author = {Xiang Xiang Wang and Sean Cottrell and Guo-Wei Wei},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12805},
eprint = {2609.12805},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12805},
}
RIS
TY - UNPB
TI - Data-Adaptive Grassmann Manifold Representations for Spatial Transcriptomics Alignment
AU - Xiang Xiang Wang
AU - Sean Cottrell
AU - Guo-Wei Wei
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.12805
UR - https://arxiv.org/abs/2609.12805
AB - Spatial transcriptomics measures gene expression together with spatial coordinates, but many existing analysis methods represent each spot primarily by a single feature vector. We propose GrassST, a subspace method for spatial transcriptomics analysis and cross-slice alignment. For each spatial spot, GrassST constructs a neighborhood from tissue coordinates, fits a low-dimensional subspace to the embedded expression profiles in that neighborhood, and represents the spot by the resulting subspace. These representations are points on a Grassmann manifold and can be compared using distances between subspaces. GrassST selects the neighborhood size and subspace rank from the spectral energy of the data, allowing these parameters to vary across datasets rather than being fixed globally. Experiments on four spatial transcriptomics datasets show that GrassST achieves competitive clustering and cross-slice integration performance under a unified evaluation pipeline.
ER -