Evidence map›Paper›PMID 36303786›Full record

ArticleFrontiers in bioinformatics2021

K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods.

Kristen Feher, Matthew S Graus, Simao Coelho, Megan V Farrell, Jesse Goyette, Katharina Gaus

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in bioinformatics, 2021. 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

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

6 authors.

Kristen FeherSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.
Matthew S GrausSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.
Simao CoelhoSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.
Megan V FarrellSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.
Jesse GoyetteSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.
Katharina GausSchool of Medical Sciences, EMBL Australia Node in Single Molecule Science, University of New South Wales, Kensington, NSW, Australia.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

Single molecule localisation microscopy (SMLM) is a powerful tool that has revealed the spatial arrangement of cell surface signalling proteins, producing data of enormous complexity. The complexity is partly driven by the convolution of technical and biological signal components, and partly by the challenge of pooling information across many distinct cells. To address these two particular challenges, we have devised a novel algorithm called K-neighbourhood analysis (KNA), which emphasises the fact that each image can also be viewed as a composition of local neighbourhoods. KNA is based on a novel transformation, spatial neighbourhood principal component analysis (SNPCA), which is defined by the PCA of the normalised

Indexed as

clusteringimage analysislocal density estimationlocal indicators of spatial associationpoint pattern analysissingle molecule localisation microscopyTCR clustering

Identifiers

PMID36303786
PMCPMC9581049

What OpenQuestion holds

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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.