Spatial mutual nearest neighbors for spatial transcriptomics data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40713795", "kind": "science-record", "revision": 2, "content_hash": "sha256:32c080d7345309979da9c73b1177414126cf391db885d37e151b90d2f1969de5", "created_at": "2026-11-01T20:30:48Z", "updated_at": "2026-11-01T21:35:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Spatial mutual nearest neighbors for spatial transcriptomics data.", "doi": "10.1093/bioinformatics/btaf403", "pmid": "40713795", "authors": ["Haowen Zhou", "Pratibha Panwar", "Boyi Guo", "Caleb Hallinan", "Shila Ghazanfar", "Stephanie C Hicks"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information.\nRESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods.\nAVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).", "claims": [{"id": "claim-method", "statement": "We used the ARI to compare the overlap of the manual annotations at a spot/cell resolution to a predicted cell type or spatial domain. An ARI of 0 corresponds to random labeling, and an ARI of 1 corresponds to perfect agreement. We used the adjustedRandIndex() function in the mclust R package (Scrucca et al. 2023) (version 6.0.0). We also used NMI to evaluate the accuracy using our simulated SRT samples. An NMI of 0 corresponds to no mutual information and 1 corresponds to perfect correlation. 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Supplementary data are available at Bioinformatics online.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40713795", "locator": "sections[17] \"2.5 Software availability\" paragraph 2", "quote": "The complete code and data required to reproduce the analysis are available on Zenodo with the doi: 10.5281/zenodo.15073963."}, {"asset": "source:pmid:40713795", "locator": "sections[30] \"Supplementary data\" paragraph 2", "quote": "Supplementary data are available at Bioinformatics online."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40713795"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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