Evidence map›Paper›PMID 41612174›Full record

ArticleBMC bioinformatics2026

SPAC: a scalable and integrated enterprise platform for single-cell spatial analysis.

Fang Liu, Rui He, Thomas Sheeley, David A Scheiblin, Stephen J Lockett, Lisa A Ridnour, David A Wink, Mark Jensen, Janelle Cortner, George Zaki

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Fang LiuBiomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA.
Rui HeEssential Software Inc., Gaithersburg, MD, USA.
Thomas SheeleyDepartment of Biochemistry, Purdue University, West Lafayette, IN, USA.
David A ScheiblinOptical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Stephen J LockettOptical Microscopy and Analysis Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Lisa A RidnourCancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.
David A WinkCancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA.
Mark JensenBiomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA.
Janelle CortnerCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA.
George ZakiBiomedical and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Rockville, MD, USA. george.zaki@nih.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvancements in spatially resolved single-cell technologies are transforming our understanding of tissue architecture and disease microenvironments. However, analyzing the resulting high-dimensional, gigabyte-scale datasets remains challenging due to fragmented workflows, intensive computational requirements, and a lack of accessible, user-friendly tools for non-technical researchers.

resultsWe introduce SPAC (analysis of SPAtial single-Cell datasets), a scalable, web-based platform for efficient and reproducible single-cell spatial analysis. SPAC employs a four-tier architecture that includes a modular Python-based analysis engine, seamless integration with high-performance computing (HPC) and GPU acceleration, an interactive browser interface for no-code workflow configuration, and a real-time visualization layer powered by Shiny for Python dashboards. This design empowers distinct user roles: data scientists can extend and customize analysis modules, while bench scientists can execute complete workflows and interactively explore results without coding. Built-in reproducibility features and collaborative workflow support ensure that analyses are transparent and easily shared across research teams. Using a 2.6-million-cell multiplex imaging dataset from a 4T1 breast tumor model as a benchmark, SPAC reduced unsupervised clustering time from ~3 hours on a CPU to under 10 minutes with GPU acceleration, achieving more than a 20-fold speedup. It also enabled fine-grained spatial profiling of distinct tumor microenvironment compartments, demonstrating the platform's scalability and performance.

conclusionsSPAC addresses major barriers in single-cell spatial analysis by uniting an intuitive, user-friendly interface with scalable, high-performance computation in a robust and reproducible framework. By streamlining complex analyses and bridging the gap between experimental and computational researchers, SPAC fosters collaborative workflows and accelerates the transformation of large-scale spatial datasets into actionable biological insights.

Indexed as

Computational BiologySingle-Cell AnalysisSoftwareAnimalsDashboard SystemsUser-Computer InterfaceHigh-performance computingInteractive visualizationMultiplex imagingScalable analysisSingle-cell analysisSpatial omicsSpatial proteomicsTumor microenvironment

Identifiers

PMID41612174
PMCPMC12857135

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.