Evidence map›Paper›PMID 40957414›Full record

ArticleCell reports methods2025

Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.

Farhan Ameen, Nick Robertson, David M Lin, Shila Ghazanfar, Ellis Patrick

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In one paragraph

Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

5 authors.

Farhan AmeenThe Westmead Institute for Medical Research, Sydney, NSW 2145, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Nick RobertsonSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
David M LinDepartment of Biomedical Sciences, Cornell University, Ithaca, NY 14850, USA.
Shila GhazanfarSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Ellis PatrickThe Westmead Institute for Medical Research, Sydney, NSW 2145, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia. Electronic address: ellis.patrick@sydney.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial proteomic and transcriptomic technologies enable high-throughput phenotyping of cells in situ, enabling quantification of spatial relationships among diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method that considers alternative frames of reference for contextualizing spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells that are consistent across regions. By modeling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and transcriptomics datasets that modeling spatial relationships this way is biologically meaningful and can improve the prediction of patient prognosis in a classification setting.

Indexed as

ProteomicsGene Expression ProfilingHumansTranscriptomeBioconductorCP: Computational biologyCP: Systems biologydata analysismultiplexed imagingpoint patternsspatial analysisspatial omicsspatial statisticstissue microenvironment

Identifiers

PMID40957414
PMCPMC12570319

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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.