Evidence map›Paper›PMID 39657949›Full record

ArticleBioinformatics (Oxford, England)2024

SuperSpot: coarse graining spatial transcriptomics data into metaspots.

Matei Teleman, Aurélie A G Gabriel, Léonard Hérault, David Gfeller

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Matei TelemanDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne 1011, Switzerland.ORCID 0000-0001-7834-3992
Aurélie A G GabrielDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne 1011, Switzerland.
Léonard HéraultDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne 1011, Switzerland.
David GfellerDepartment of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne 1011, Switzerland.ORCID 0000-0002-3952-0930

Funding

Swiss National Science Foundation 310030_197514
6 · The paper itself

Abstract

summarySpatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions of spots (also called cells or bins depending on the technologies). Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptomically similar spots into "metaspots". The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptomic similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies. AVAILABILITY AND IMPLEMENTATION: SuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot and archived on Zenodo (https://doi.org/10.5281/zenodo.14222088). The code to reproduce the figures is available at https://github.com/GfellerLab/SuperSpot/tree/main/figures (https://doi.org/10.5281/zenodo.14222088).

Indexed as

Computational BiologyGene Expression ProfilingSoftwareTranscriptomeCluster Analysis

Identifiers

PMID39657949
PMCPMC11725322

What OpenQuestion holds

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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.