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Peer-reviewedCC BYBioinformatics

hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data

  • Ning Liu1,2,3,4,5
  • Jarryd Martin2,3,6,7
  • Dharmesh Dinesh Bhuva2,3,4,5
  • Jinjin Chen2,3,6,7
  • Mengbo Li2,3
  • Sam Lee2,3,6,7
  • Malvika Kharbanda4,5
  • Jinming Cheng2,3,6,7
  • Ahmed Mohamed2,3,8
  • Arutha Kulasinghe9,10
  • Yunshun Chen2,3,11
  • Chin Wee Tan2,3,9,6,7,12
3 more authors
  • Fuyi Li4,5
  • Jose Maria Polo1,13,4
  • Melissa J. Davis2,14,3,4
14 affiliations
  1. 1Adelaide Centre for Epigenetics, School of Biomedicine, Faculty of Health and Medical Sciences, The University of Adelaide , SA 5005,
  2. 2Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research , Parkville, Melbourne, Victoria 3052,
  3. 3Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne , Parkville, VIC 3010,
  4. 4South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide , Adelaide, SA 5005,
  5. 5SAiGENCI
  6. 6Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
  7. 7Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
  8. 8The Walter and Eliza Hall Institute of Medical Research
  9. 9Frazer Institute, Faculty of Medicine, The University of Queensland , Brisbane, Queensland 4102,
  10. 10The University of Queensland
  11. 11Walter and Eliza Hall Institute of Medical Research
  12. 12Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
  13. 13Department of Anatomy and Developmental Biology, Monash University , Clayton, VIC 3800,
  14. 14Computational Biology, Isomorphic Labs , Bishopsgate, London, EC2M 4RB,
Open at sourceDOICite

Abstract

MOTIVATION: Understanding complex cellular neighborhoods has provided new insights into tissue biology. Accurate neighborhood identification is crucial, yet existing methods often focus on hard assignment and do not generate cell-specific neighborhood profiles at single-cell level. RESULTS: We developed hoodscanR, a Bioconductor package identifying and analyzing cellular neighborhoods in spatial data. The central output of hoodscanR is a cell-level neighborhood probability profile, which represents partial membership of each cell across multiple annotation-defined neighborhoods. This probabilistic representation supports downstream analyses including neighborhood visualization, uncertainty assessment, neighborhood-based clustering and neighborhood-aware differential expression. Applying hoodscanR to breast and lung cancer datasets, we showcase its ability to characterize mixed tissue environments and identify transcriptional changes in tumor cells from distinct spatial neighborhoods. AVAILABILITY: The hoodscanR package is publicly available in Bioconductor at https://bioconductor.org/packages/release/bioc/html/hoodscanR.html.

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Cite

BibTeXRIS
BibTeX
@article{liu2026hoodscanr,
  title = {{hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data}},
  author = {Ning Liu and Jarryd Martin and Dharmesh Dinesh Bhuva and Jinjin Chen and Mengbo Li and Sam Lee and Malvika Kharbanda and Jinming Cheng and Ahmed Mohamed and Arutha Kulasinghe and Yunshun Chen and Chin Wee Tan and Fuyi Li and Jose Maria Polo and Melissa J. Davis},
  journal = {Bioinformatics},
  year = {2026},
  month = aug,
  doi = {10.1093/bioinformatics/btag609},
  url = {https://doi.org/10.1093/bioinformatics/btag609},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data
AU  - Ning Liu
AU  - Jarryd Martin
AU  - Dharmesh Dinesh Bhuva
AU  - Jinjin Chen
AU  - Mengbo Li
AU  - Sam Lee
AU  - Malvika Kharbanda
AU  - Jinming Cheng
AU  - Ahmed Mohamed
AU  - Arutha Kulasinghe
AU  - Yunshun Chen
AU  - Chin Wee Tan
AU  - Fuyi Li
AU  - Jose Maria Polo
AU  - Melissa J. Davis
PY  - 2026
DA  - 2026/08/01
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btag609
UR  - https://doi.org/10.1093/bioinformatics/btag609
AB  - MOTIVATION: Understanding complex cellular neighborhoods has provided new insights into tissue biology. Accurate neighborhood identification is crucial, yet existing methods often focus on hard assignment and do not generate cell-specific neighborhood profiles at single-cell level. RESULTS: We developed hoodscanR, a Bioconductor package identifying and analyzing cellular neighborhoods in spatial data. The central output of hoodscanR is a cell-level neighborhood probability profile, which represents partial membership of each cell across multiple annotation-defined neighborhoods. This probabilistic representation supports downstream analyses including neighborhood visualization, uncertainty assessment, neighborhood-based clustering and neighborhood-aware differential expression. Applying hoodscanR to breast and lung cancer datasets, we showcase its ability to characterize mixed tissue environments and identify transcriptional changes in tumor cells from distinct spatial neighborhoods. AVAILABILITY: The hoodscanR package is publicly available in Bioconductor at https://bioconductor.org/packages/release/bioc/html/hoodscanR.html.
SN  - 1367-4803
SN  - 1367-4811
ER  -