Evidence map›Paper›PMID 40840441›Full record

ArticleCell reports methods2025

Spatial dissimilarity analysis in single-cell transcriptomics.

Quan Shi, Karsten Kristiansen

Abstract read
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 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

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

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

2 authors.

Quan ShiLaboratory of Integrative Biomedicine, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark. Electronic address: quan.shi@bio.ku.dk.
Karsten KristiansenLaboratory of Integrative Biomedicine, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark; BGI-Shenzhen, Shenzhen 518103, China; Institute of Metagenomics, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao 166555, China. Electronic address: kk@bio.ku.dk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We develop the spatial dissimilarity method to uncover complex bivariate relationships in single-cell and spatial transcriptomics data, addressing challenges such as alternative splicing and allele-specific gene expression. Applying this method to detect alternative splicing in neurons demonstrates improved accuracy and sensitivity compared to existing tools, notably identifying neuron subtypes. In tumor cells, spatial dissimilarity analysis reveals somatic variants that emerge during tumor progression, validated through whole-exome sequencing. These findings highlight how allele-specific genetic variants contribute to the subclone architecture of cancer cells, offering insights into cellular heterogeneity. Applied on a human cell atlas, we uncover numerous cases of allele-specific expression of genes in normal cells. We provide a software package for spatial dissimilarity analysis to enable enhanced understanding of cellular complexity and gene expression dynamics under homeostatic conditions and during states of transitions.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisTranscriptomeAllelesAlternative SplicingExome SequencingHumansNeuronsSoftwareallele-specific gene expressionalternative splicingcancer genomicsCP: computational biologyCP: systems biologylineage trajectorysingle-cell genomicssingle-cell RNA-seqsomatic mosaicismspatial transcriptomics

Identifiers

PMID40840441
PMCPMC12539255

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.