hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data.

Authors

Ning Liu, Jarryd Martin, Dharmesh D Bhuva, Jinjin Chen, Mengbo Li, Samuel C Lee, Malvika Kharbanda, Jinming Cheng, Ahmed Mohamed, Arutha Kulasinghe, Yunshun Chen, Chin Wee Tan, Fuyi Li, Jose M Polo, Melissa J Davis

Published in

Bioinformatics (Oxford, England). Aug 13, 2026. Epub Aug 13, 2026.

Abstract

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.
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.
The hoodscanR package is publicly available in Bioconductor at https://bioconductor.org/packages/release/bioc/html/hoodscanR.html.
Supplementary data are available at Bioinformatics online.

PMID:
42596574
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.

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