Inferring spatially transient gene expression pattern from spatial transcriptomic studies
Jan KueckelhausiD, Jasmin von EhriD, Vidhya Madapusi RaviiD, Paulina WilliD, Kevin JosephiD, Jürgen BeckiD, Ulrich HofmanniD, Daniel DeleviD, Oliver SchnelliD, Dieter Henrik HeilandiD
Abstract
Spatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization. Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression, regional adaptation of transcriptional programs and transient dynamics along spatial trajectories.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 48
- Assay
- Research Focus
- Approach
Abstract summary
Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract · CC BY-NC-ND 4.0
SPATA bundles spatial transcriptomics analysis: spatially resolved expression, how transcriptional programs shift by region, and expression changes along spatial trajectories.
Spatial transcriptomics gives deep transcriptomic profiling while preserving spatial organization. SPATA is offered as one framework spanning spatially resolved expression, regional adaptation of programs, and transitions along spatial trajectories.
Key results
ClaimedSPATA is released as a framework whose stated scope covers spatially resolved gene expression, region-dependent adaptation of transcriptional programs, and dynamics along spatial trajectories.
Described in the abstract with a project address; no datasets, figures, benchmarks or comparisons against other spatial analysis software are reported there. · Abstract
ClaimedTransient expression changes are to be read along trajectories defined through the tissue, which is the capability the title puts forward.
Named as part of the framework's scope in the abstract; no algorithm, example trajectory or validation is given. · Abstract
Methods, data and reproducibility
A software framework for spatial transcriptomic analysis that characterizes expression with its spatial coordinates, examines how transcriptional programs adapt between regions, and follows expression along trajectories through the tissue. The abstract names no algorithms, input platforms or test datasets.
- Methods & tools
- SPATA
- Reproducibility gaps
- A project page is printed, but the abstract names no example dataset, release version, language or dependencies.
Limitations (2)
- The abstract is a tool announcement: it reports no dataset, benchmark, runtime or comparison with other spatial transcriptomics frameworks. · noted in this summary
- Neither the supported assay platforms nor the methods behind the trajectory analysis are stated, so fit to a given dataset cannot be judged from this text. · noted in this summary
Who should read:Read this if you analyse spatial transcriptomics data and want a framework aimed at regional programme differences and expression dynamics along spatial trajectories.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies
Shiquan Sun, Jiaqiang Zhu, Xiang Zhou·Nature MethodsPeer-reviewed·Jan 27, 2020
- STAMarker: Determining spatial domain-specific variable genes with saliency maps in deep learning
Chihao Zhang, Kangning Dong, … Shihua Zhang·Nucleic Acids ResearchPeer-reviewed·Sep 19, 2023
- Museum of Spatial Transcriptomics
Lambda Moses, Lior S. Pachter·Nature MethodsPeer-reviewed·Mar 10, 2022
- SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs
Adrien Osakwe, Wenqi Dong, … Yue Li·Briefings in BioinformaticsPeer-reviewed·Nov 1, 2025
- Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Jian Hu, Xiangjie Li, … Mingyao Li·bioRxiv·Nov 30, 2020
- Spatial pattern and differential expression analysis with spatial transcriptomic data
Fei Qin, Xizhi Luo, … Guoshuai Cai·Nucleic Acids ResearchPeer-reviewed·Oct 11, 2024