{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:26:39Z","timestamp":1773494799703,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012254","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","award":["CRC-2021-00482"],"award-info":[{"award-number":["CRC-2021-00482"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2021-04072"],"award-info":[{"award-number":["RGPIN-2021-04072"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012254","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T21:10:46Z","timestamp":1719522646000},"page":"e1012254","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning"],"prefix":"10.1371","volume":"20","author":[{"given":"Youcheng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leann","family":"Lac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-2245","authenticated-orcid":true,"given":"Pingzhao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"pcbi.1012254.ref001","doi-asserted-by":"crossref","first-page":"785290","DOI":"10.3389\/fgene.2021.785290","article-title":"Analysis and visualization of spatial transcriptomic data","volume":"12","author":"B Liu","year":"2022","journal-title":"Frontiers in Genetics"},{"issue":"6","key":"pcbi.1012254.ref002","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/s41587-020-0469-4","article-title":"Benchmarking single-cell RNA-sequencing protocols for cell atlas projects","volume":"38","author":"E Mereu","year":"2020","journal-title":"Nature Biotechnology"},{"key":"pcbi.1012254.ref003","first-page":"e27041","volume":"6","author":"A Regev","year":"2017","journal-title":"The Human Cell Atlas. eLife"},{"key":"pcbi.1012254.ref004","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1038\/s41467-020-18158-5","article-title":"Single cell transcriptomics comes of age","volume":"11","author":"S Aldridge","year":"2020","journal-title":"Nature Communication"},{"issue":"7751","key":"pcbi.1012254.ref005","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41586-019-1049-y","article-title":"Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH","volume":"568","author":"CL Eng","year":"2019","journal-title":"Nature"},{"issue":"11","key":"pcbi.1012254.ref006","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1038\/s41592-018-0175-z","article-title":"Spatial organization of the somatosensory cortex revealed by osmFISH","volume":"15","author":"S Codeluppi","year":"2018","journal-title":"Nature methods"},{"issue":"39","key":"pcbi.1012254.ref007","doi-asserted-by":"crossref","first-page":"19490","DOI":"10.1073\/pnas.1912459116","article-title":"Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression","volume":"116","author":"C Xia","year":"2019","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"6434","key":"pcbi.1012254.ref008","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1126\/science.aaw1219","article-title":"Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution","volume":"363","author":"SG Rodriques","year":"2019","journal-title":"Science"},{"key":"pcbi.1012254.ref009","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1038\/s41592-019-0548-y","article-title":"High-definition spatial transcriptomics for in situ tissue profiling.","volume":"16","author":"S Vickovic","year":"2019","journal-title":"Nat Methods"},{"issue":"6444","key":"pcbi.1012254.ref010","doi-asserted-by":"crossref","first-page":"eaas9536","DOI":"10.1126\/science.aas9536","article-title":"Spatiotemporal structure of cell fate decisions in murine neural crest","volume":"364","author":"R Soldatov","year":"2019","journal-title":"Science"},{"issue":"4","key":"pcbi.1012254.ref011","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.cell.2020.06.038","article-title":"Spatial transcriptomics and in situ sequencing to study Alzheimer\u2019s disease","volume":"182","author":"WT Chen","year":"2020","journal-title":"Cell"},{"issue":"1","key":"pcbi.1012254.ref012","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1038\/s41592-019-0631-4","article-title":"Probabilistic cell typing enables fine mapping of closely related cell types in situ","volume":"17","author":"X Qian","year":"2020","journal-title":"Nature methods"},{"issue":"6233","key":"pcbi.1012254.ref013","article-title":"RNA imaging. 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