Evidence map›Paper›PMID 40167331›Full record

ArticleNucleic acids research2025

CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization.

Sokratia Georgaka, William Geraint Morgans, Qian Zhao, Diego Sanchez Martinez, Amin Ali, Mohamed Ghafoor, Syed-Murtuza Baker, Robert G Bristow, Mudassar Iqbal, Magnus Rattray

Abstract read
In one paragraph

Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025
    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

10 authors.

Sokratia GeorgakaDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0002-0078-8083
William Geraint MorgansDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0001-8292-7106
Qian ZhaoDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.
Diego Sanchez MartinezCRUK Manchester Institute, University of Manchester, Manchester M20 4BX, United Kingdom.
Amin AliDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.
Mohamed GhafoorDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.
Syed-Murtuza BakerDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.
Robert G BristowDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.
Mudassar IqbalDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0002-5006-4331
Magnus RattrayDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID 0000-0001-8196-5565

Funding

CRUK Manchester Institute C5759/A27412MRC MR/X014088/1Prostate Cancer UK MA-COE128-002Wellcome Institutional Strategic Support Fund (Wellcome ISSF) 204796/Z/16/ZWellcome TrustWellcome Trust 204832/B/16/Z
6 · The paper itself

Abstract

Spatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics (ST) technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here, we present CellPie, a fast, unsupervised factor discovery method based on joint non-negative matrix factorization of spatial RNA transcripts and histological image features. CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional ST datasets. We assessed CellPie on three different human cancer types with different spatial resolutions, including a highly resolved Visium HD dataset, demonstrating both good performance and high computational efficiency compared to existing methods.

Indexed as

Computational BiologyGene Expression ProfilingNeoplasmsSoftwareTranscriptomeAlgorithmsHumansLeast-Squares Analysis

Identifiers

PMID40167331
PMCPMC12086691

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.