Evidence map›Paper›PMID 37953397›Full record

ArticleNucleic acids research2024

standR: spatial transcriptomic analysis for GeoMx DSP data.

Ning Liu, Dharmesh D Bhuva, Ahmed Mohamed, Micah Bokelund, Arutha Kulasinghe, Chin Wee Tan, Melissa J Davis

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
10.0field-weighted citation impact, top 1% of its field
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

43 citing papers in PubMed, 65 citations in OpenAlex.

  1. Emerging technologies in the spatial proteomics landscape.Analytical and bioanalytical chemistry · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. TNF-⍺-mediated myeloid-instructed CD14Cell reports. Medicine · 2026
    Article
  17. The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026
    Review
  18. Article
  19. Review
  20. Article
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

7 authors at 3 institutions in 1 country.

Ning LiuBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.
Dharmesh D BhuvaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.ORCID 0000-0002-6398-9157
Ahmed MohamedBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.ORCID 0000-0001-6507-5300
Micah BokelundBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.
Arutha KulasingheFrazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia.
Chin Wee TanBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.ORCID 0000-0001-9695-7218
Melissa J DavisBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia.ORCID 0000-0003-4864-7033
The University of Melbourne · AUThe University of Queensland · AUWalter and Eliza Hall Institute of Medical Research · AU

Funding

Australian Academy of SciencesAustralian Lions Childhood Cancer FoundationBetty Smyth Centenary Fellowship in BioinformaticsCancer Council VictoriaCure Brain Cancer Foundation and National Breast Cancer Foundation CBCNBCF-19-009Davis labOperational Infrastructure Program of the Victorian Government
6 · The paper itself

Abstract

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

Indexed as

Gene Expression ProfilingSoftwareTranscriptomeComputational BiologyIntracellular SpaceReproducibility of ResultsWorkflow

Identifiers

PMID37953397
PMCPMC10783521
OpenAlexW4388615764

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.