ArticleBMC bioinformatics2020
SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
Who cites it
10 citing papers in PubMed.
- spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation.Genome biology · 2025Article
- SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.Briefings in bioinformatics · 2024Article
- Advances in spatial transcriptomics and related data analysis strategies.Journal of translational medicine · 2023Review
- A guidebook of spatial transcriptomic technologies, data resources and analysis approaches.Computational and structural biotechnology journal · 2023Review
- Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight.Cell reports methods · 2022Review
- Clinical and translational values of spatial transcriptomics.Signal transduction and targeted therapy · 2022Review
- Spatial components of molecular tissue biology.Nature biotechnology · 2022Review
- Spatial omics: Navigating to the golden era of cancer research.Clinical and translational medicine · 2022Review
- Spatial omics and multiplexed imaging to explore cancer biology.Nature methods · 2021Review
- Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis.Frontiers in neuroscience · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundTechnological developments in the emerging field of spatial transcriptomics have opened up an unexplored landscape where transcript information is put in a spatial context. Clustering commonly constitutes a central component in analyzing this type of data. However, deciding on the number of clusters to use and interpreting their relationships can be difficult.
resultsWe introduce SpatialCPie, an R package designed to facilitate cluster evaluation for spatial transcriptomics data. SpatialCPie clusters the data at multiple resolutions. The results are visualized with pie charts that indicate the similarity between spatial regions and clusters and a cluster graph that shows the relationships between clusters at different resolutions. We demonstrate SpatialCPie on several publicly available datasets.
conclusionsSpatialCPie provides intuitive visualizations of cluster relationships when dealing with Spatial Transcriptomics data.
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Registered trials
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.