{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T04:35:54Z","timestamp":1768710954005,"version":"3.49.0"},"reference-count":80,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P20GM130454"],"award-info":[{"award-number":["P20GM130454"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P20GM104416"],"award-info":[{"award-number":["P20GM104416"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DoD","award":["HT94252310267"],"award-info":[{"award-number":["HT94252310267"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large\u2013scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40\u00d7-resolution imaging, and joining of multiple patients\u2019 tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation\/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.<\/jats:p>","DOI":"10.1093\/bib\/bbae476","type":"journal-article","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T05:38:56Z","timestamp":1728106736000},"source":"Crossref","is-referenced-by-count":8,"title":["An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology"],"prefix":"10.1093","volume":"25","author":[{"given":"Michael Y","family":"Fatemi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA 22903,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunrui","family":"Lu","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6471-4630","authenticated-orcid":false,"given":"Alos B","family":"Diallo","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Department of Epidemiology, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]},{"name":"Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gokul","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zarif L","family":"Azher","sequence":"additional","affiliation":[{"name":"Thomas Jefferson High School for Science and Technology , Alexandria, VA 22312,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brock C","family":"Christensen","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2279-4097","authenticated-orcid":false,"given":"Lucas A","family":"Salas","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory J","family":"Tsongalis","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott M","family":"Palisoul","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Perreard","sequence":"additional","affiliation":[{"name":"Genomics Shared Resource, Dartmouth Cancer Center , Lebanon, NH 03756,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"IV","given":"Fred W","family":"Kolling","sequence":"additional","affiliation":[{"name":"Genomics Shared Resource, Dartmouth Cancer Center , Lebanon, NH 03756,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louis J","family":"Vaickus","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8050-1291","authenticated-orcid":false,"given":"Joshua J","family":"Levy","sequence":"additional","affiliation":[{"name":"Emerging Diagnostic and Investigative Technologies , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Dartmouth Health , Department of Pathology and Laboratory Medicine, , Lebanon, NH 03766,","place":["USA"]},{"name":"Department of Epidemiology, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]},{"name":"Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine , Hanover, NH 03756,","place":["USA"]},{"name":"Department of Dermatology, Dartmouth Health , Lebanon, NH 03756,","place":["USA"]},{"name":"Department of Pathology and Laboratory Medicine, Cedars Sinai Medical Center , Los Angeles, CA 90048,","place":["USA"]},{"name":"Department of Computational Biomedicine, Cedars Sinai Medical Center , Los Angeles, CA 90048,","place":["USA"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"2024100505384665500_ref1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1179\/his.2006.29.2.99","article-title":"A short history of histopathology technique","volume":"29","author":"Titford","year":"2006","journal-title":"J Histotechnol"},{"key":"2024100505384665500_ref2"},{"key":"2024100505384665500_ref3","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s00253-020-11056-2","article-title":"Bioinformatics: new tools and applications in life science and personalized medicine","volume":"105","author":"Branco","year":"2021","journal-title":"Appl Microbiol Biotechnol"},{"key":"2024100505384665500_ref4","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1093\/bib\/bby063","article-title":"A brief history of bioinformatics","volume":"20","author":"Gauthier","year":"2019","journal-title":"Brief Bioinform"},{"key":"2024100505384665500_ref5","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1354\/vp.42-4-405","article-title":"Technical aspects of immunohistochemistry","volume":"42","author":"Ramos-Vara","year":"2005","journal-title":"Vet Pathol"},{"key":"2024100505384665500_ref6","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nat Methods"},{"key":"2024100505384665500_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-020-06832-3","article-title":"Seamless integration of image and molecular analysis for spatial transcriptomics workflows","volume":"21","author":"Bergenstr\u00e5hle","year":"2020","journal-title":"BMC Genomics"},{"key":"2024100505384665500_ref8","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1038\/s41587-023-01697-9","article-title":"High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE","volume":"41","author":"Vahid","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2024100505384665500_ref9","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1038\/s41592-021-01264-7","article-title":"Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram","volume":"18","author":"Biancalani","year":"2021","journal-title":"Nat Methods"},{"key":"2024100505384665500_ref10","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1038\/s41467-020-15968-5","article-title":"Inferring spatial and signaling relationships between cells from single cell transcriptomic data","volume":"11","author":"Cang","year":"2020","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref11","doi-asserted-by":"publisher","first-page":"1843","DOI":"10.1101\/gr.271288.120","article-title":"Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities","volume":"31","author":"Miller","year":"2021","journal-title":"Genome Res"},{"key":"2024100505384665500_ref12","doi-asserted-by":"publisher","first-page":"4050","DOI":"10.1038\/s41467-023-39895-3","article-title":"Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref13","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1038\/s41467-023-37168-7","article-title":"A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics","volume":"14","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref14","doi-asserted-by":"publisher","DOI":"10.1002\/cpz1.405","article-title":"Analyzing spatial Transcriptomics data using Giotto","volume":"2","author":"Del Rossi","year":"2022","journal-title":"Current Protocols"},{"key":"2024100505384665500_ref15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s13059-021-02286-2","article-title":"Giotto: a toolbox for integrative analysis and visualization of spatial expression data","volume":"22","author":"Dries","year":"2021","journal-title":"Genome Biol"},{"key":"2024100505384665500_ref16","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1038\/s41587-022-01288-0","article-title":"DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data","volume":"40","author":"Jerby-Arnon","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024100505384665500_ref17","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1038\/s41587-021-00935-2","article-title":"Spatial transcriptomics at subspot resolution with BayesSpace","volume":"39","author":"Zhao","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2024100505384665500_ref18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41587-023-02019-9","article-title":"Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology","volume":"42","author":"Zhang","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2024100505384665500_ref19","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.cels.2023.03.008","article-title":"Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA","volume":"14","author":"Hu","year":"2023","journal-title":"Cell systems"},{"key":"2024100505384665500_ref20","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.cels.2023.06.003","article-title":"BayesTME: an end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment","volume":"14","author":"Zhang","year":"2023","journal-title":"Cell Systems"},{"key":"2024100505384665500_ref21","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1038\/s41551-019-0362-y","article-title":"Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning","volume":"3","author":"Rivenson","year":"2019","journal-title":"Nat Biomed Eng"},{"key":"2024100505384665500_ref22","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1038\/s41379-020-00718-1","article-title":"A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies","volume":"34","author":"Levy","year":"2021","journal-title":"Mod Pathol"},{"key":"2024100505384665500_ref23","first-page":"302","volume-title":"Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020)\u2014Volume 3: BIOINFORMATICS","author":"Levy","year":"2020"},{"key":"2024100505384665500_ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100308","article-title":"Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: a pilot study","volume":"14","author":"Fatemi","year":"2023","journal-title":"J Pathol Inform"},{"key":"2024100505384665500_ref25","first-page":"477","article-title":"Potential to enhance large scale molecular assessments of skin photoaging through virtual inference of spatial transcriptomics from routine staining","volume":"2023","author":"Srinivasan","year":"2024","journal-title":"Biocomputing"},{"key":"2024100505384665500_ref26","doi-asserted-by":"publisher","first-page":"bbac297","DOI":"10.1093\/bib\/bbac297","article-title":"Spatial transcriptomics prediction from histology jointly through transformer and graph neural networks","volume":"23","author":"Zeng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024100505384665500_ref27","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1038\/s41551-020-0578-x","article-title":"Integrating spatial gene expression and breast tumour morphology via deep learning","volume":"4","author":"He","year":"2020","journal-title":"Nat Biomed Eng"},{"key":"2024100505384665500_ref28","first-page":"70626","article-title":"Spatially resolved gene expression prediction from histology images via bi-modal contrastive learning","volume":"36","author":"Xie","year":"2023","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024100505384665500_ref29","doi-asserted-by":"publisher","DOI":"10.1101\/2023.09.20.558624","article-title":"Generalization of deep learning models for predicting spatial gene expression profiles using histology images: a breast cancer case study","author":"Jiang","year":"2023"},{"key":"2024100505384665500_ref30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07685-4","article-title":"Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation","volume":"12","author":"Monjo","year":"2022","journal-title":"Sci Rep"},{"key":"2024100505384665500_ref31","doi-asserted-by":"publisher","first-page":"13604","DOI":"10.1038\/s41598-023-40219-0","article-title":"Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning","volume":"13","author":"Rahaman","year":"2023","journal-title":"Sci Rep"},{"key":"2024100505384665500_ref32","doi-asserted-by":"publisher","first-page":"bbad464","DOI":"10.1093\/bib\/bbad464","article-title":"THItoGene: a deep learning method for predicting spatial transcriptomics from histological images","volume":"25","author":"Jia","year":"2024","journal-title":"Brief Bioinform"},{"key":"2024100505384665500_ref33","doi-asserted-by":"publisher","DOI":"10.1101\/2023.10.09.23296701","article-title":"Feasibility of inferring spatial transcriptomics from single-cell histological patterns for studying colon cancer tumor heterogeneity","author":"Fatemi","year":"2023"},{"key":"2024100505384665500_ref34","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1038\/s41698-023-00477-7","article-title":"Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site","volume":"8","author":"Levy","year":"2024","journal-title":"NPJ Precis Oncol"},{"key":"2024100505384665500_ref35","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/1749-8090-2-45","article-title":"Fixation artefact in an intra-operative frozen section: a potential cause of misinterpretation","volume":"2","author":"Thomson","year":"2007","journal-title":"J Cardiothorac Surg"},{"key":"2024100505384665500_ref36","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1111\/cup.14481","article-title":"Artificial intelligence and frozen section histopathology: a systematic review","volume":"50","author":"Gorman","year":"2023","journal-title":"J Cutan Pathol"},{"key":"2024100505384665500_ref37"},{"key":"2024100505384665500_ref38","doi-asserted-by":"publisher","first-page":"33","DOI":"10.4103\/jpi.jpi_48_20","article-title":"(Re)defining the high-power field for digital pathology","volume":"11","author":"Kim","year":"2020","journal-title":"J Pathol Inform"},{"key":"2024100505384665500_ref39","doi-asserted-by":"publisher","first-page":"4692","DOI":"10.1158\/1538-7445.AM2023-4692","article-title":"Abstract 4692: comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist","volume":"83","author":"Rosasco","year":"2023","journal-title":"Cancer Res"},{"key":"2024100505384665500_ref40","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2022-SITC2022.0064","article-title":"64 spatial whole transcriptome profiling of the tumor microenvironment in archived and freshly-mounted FFPE tissues","author":"Chiang","year":"2022"},{"key":"2024100505384665500_ref41","author":"Principles of Cancer Staging"},{"key":"2024100505384665500_ref42","doi-asserted-by":"publisher","first-page":"4904","DOI":"10.3892\/etm.2019.8146","article-title":"Tumor-infiltrating lymphocytes in primary tumors of colorectal cancer and their metastases","volume":"18","author":"Jakubowska","year":"2019","journal-title":"Exp Ther Med"},{"key":"2024100505384665500_ref43","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1097\/00000658-200210000-00003","article-title":"A new TNM staging strategy for node-positive (stage III) colon cancer: an analysis of 50,042 patients","volume":"236","author":"Greene","year":"2002","journal-title":"Ann Surg"},{"key":"2024100505384665500_ref44","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.2147\/CMAR.S165188","article-title":"P-TNM staging system for colon cancer: combination of P-stage and AJCC TNM staging system for improving prognostic prediction and clinical management","volume":"10","author":"Liu","year":"2018","journal-title":"CMAR"},{"key":"2024100505384665500_ref45","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.1038\/s41467-020-17083-x","article-title":"Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks","volume":"11","author":"Uttam","year":"2020","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref46","doi-asserted-by":"publisher","first-page":"pkaa023","DOI":"10.1093\/jncics\/pkaa023","article-title":"Contribution of immunoscore and molecular features to survival prediction in stage III colon cancer","volume":"4","author":"Sinicrope","year":"2020","journal-title":"JNCI Cancer Spectrum"},{"key":"2024100505384665500_ref47","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41568-020-0285-7","article-title":"The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy","volume":"20","author":"Bruni","year":"2020","journal-title":"Nat Rev Cancer"},{"key":"2024100505384665500_ref48","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1038\/s41591-018-0014-x","article-title":"Understanding the tumor immune microenvironment (TIME) for effective therapy","volume":"24","author":"Binnewies","year":"2018","journal-title":"Nat Med"},{"key":"2024100505384665500_ref49","doi-asserted-by":"publisher","first-page":"6283","DOI":"10.1007\/s13277-014-1831-2","article-title":"Immunohistochemical expression pattern of MMR protein can specifically identify patients with colorectal cancer microsatellite instability","volume":"35","author":"Amira","year":"2014","journal-title":"Tumour Biol"},{"key":"2024100505384665500_ref50","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-1-0716-1099-2_2","article-title":"The Illumina sequencing protocol and the NovaSeq 6000 system","volume":"2242","author":"Modi","year":"2021","journal-title":"Bacterial Pangenomics"},{"key":"2024100505384665500_ref51","first-page":"1","article-title":"Image quality assessment of large tissue samples stained using a customized automated slide Stainer","volume":"2023","author":"Sithambaram","year":"2023","journal-title":"MeMeA"},{"key":"2024100505384665500_ref52","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1038\/nmeth.4636","article-title":"SpatialDE: identification of spatially variable genes","volume":"15","author":"Svensson","year":"2018","journal-title":"Nat Methods"},{"key":"2024100505384665500_ref53","volume-title":"Pacific Symposium on Biocomputing"},{"key":"2024100505384665500_ref54","volume-title":"Pac Symp Biocomput"},{"key":"2024100505384665500_ref55","doi-asserted-by":"publisher","first-page":"100741","DOI":"10.1016\/j.patter.2023.100741","article-title":"Application of aligned-UMAP to longitudinal biomedical studies","volume":"4","author":"Dadu","year":"2023","journal-title":"Patterns"},{"key":"2024100505384665500_ref56","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: uniform manifold approximation and projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J Open Source Softw"},{"key":"2024100505384665500_ref57","first-page":"13","volume-title":"Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics","author":"Birnbaum","year":"1956"},{"key":"2024100505384665500_ref58","first-page":"2242","article-title":"Data Shapley: equitable valuation of data for machine learning","volume":"97","author":"Ghorbani","year":"2019","journal-title":"ICML"},{"key":"2024100505384665500_ref59","first-page":"1167","article-title":"Towards efficient data valuation based on the Shapley value","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics, PMLR","author":"Jia","year":"2019"},{"key":"2024100505384665500_ref60","doi-asserted-by":"publisher","first-page":"8366","DOI":"10.1038\/s41598-021-87762-2","article-title":"Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset","volume":"11","author":"Tang","year":"2021","journal-title":"Sci Rep"},{"key":"2024100505384665500_ref61","doi-asserted-by":"publisher","DOI":"10.2466\/11.IT.3.1","article-title":"The simple difference formula: an approach to teaching nonparametric correlation","volume":"3","author":"Kerby","year":"2014","journal-title":"Compr Psychol"},{"key":"2024100505384665500_ref62","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/1471-2105-14-128","article-title":"Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool","volume":"14","author":"Chen","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2024100505384665500_ref63","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1038\/s41379-020-0526-z","article-title":"A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms","volume":"33","author":"Jackson","year":"2020","journal-title":"Mod Pathol"},{"key":"2024100505384665500_ref64","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/2153-3539.86284","article-title":"High-definition hematoxylin and eosin staining in a transition to digital pathology","volume":"2","author":"Martina","year":"2011","journal-title":"J Pathol Inform"},{"key":"2024100505384665500_ref65","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1038\/s41374-018-0057-0","article-title":"Principles and approaches for reproducible scoring of tissue stains in research","volume":"98","author":"Meyerholz","year":"2018","journal-title":"Lab Invest"},{"key":"2024100505384665500_ref66","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.1038\/s41467-021-24698-1","article-title":"The impact of site-specific digital histology signatures on deep learning model accuracy and bias","volume":"12","author":"Howard","year":"2021","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref67","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1309\/LGMT-402K-9M4G-FTVL","article-title":"Automated stainers","volume":"31","author":"Earle","year":"2000","journal-title":"Lab Med"},{"key":"2024100505384665500_ref68","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1177\/0192623320970534","article-title":"Impact of preanalytical factors during histology processing on section suitability for digital image analysis","volume":"49","author":"Chlipala","year":"2021","journal-title":"Toxicol Pathol"},{"key":"2024100505384665500_ref69","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1097\/PAI.0000000000000776","article-title":"An image analysis solution for quantification and determination of immunohistochemistry staining reproducibility","volume":"28","author":"Chlipala","year":"2020","journal-title":"Appl Immunohistochem Mol Morphol"},{"key":"2024100505384665500_ref70","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1179\/his.1990.13.3.193","article-title":"An artifact of H&E staining: the problem and its solution","volume":"13","author":"Wynnchuk","year":"1990","journal-title":"J Histotechnol"},{"key":"2024100505384665500_ref71","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.compmedimag.2015.03.005","article-title":"Appearance normalization of histology slides","volume":"43","author":"Vicory","year":"2015","journal-title":"Comput Med Imaging Graph"},{"key":"2024100505384665500_ref72","doi-asserted-by":"publisher","DOI":"10.1117\/1.3650306","article-title":"Correction of stain variations in nuclear refractive index of clinical histology specimens","volume":"16","author":"Uttam","year":"2011","journal-title":"J Biomed Opt"},{"key":"2024100505384665500_ref73","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring tissue architecture using spatial transcriptomics","volume":"596","author":"Rao","year":"2021","journal-title":"Nature"},{"key":"2024100505384665500_ref74","doi-asserted-by":"publisher","first-page":"2946","DOI":"10.3389\/fonc.2022.890410","article-title":"Challenges and opportunities for immunoprofiling using a spatial high-plex technology: the NanoString GeoMx\u00ae digital spatial profiler","volume":"12","author":"Hernandez","year":"2022","journal-title":"Front Oncol"},{"key":"2024100505384665500_ref75","doi-asserted-by":"crossref","DOI":"10.1136\/jitc-2022-SITC2022.0095","article-title":"95 characterization of human breast cancer tissue with the Xenium in situ platform reveals a novel marker for invasiveness","volume-title":"J Immunother Cancer","author":"Henley","year":"2022"},{"key":"2024100505384665500_ref76","doi-asserted-by":"publisher","DOI":"10.1101\/2023.02.13.528102","article-title":"Optimizing Xenium in situ data utility by quality assessment and best practice analysis workflows.","author":"Marco Salas","year":"2023"},{"key":"2024100505384665500_ref77","first-page":"1107","article-title":"A method for normalizing histology slides for quantitative analysis","volume":"2009","author":"Macenko","year":"2009","journal-title":"ISBI"},{"key":"2024100505384665500_ref78","doi-asserted-by":"publisher","first-page":"4122","DOI":"10.1038\/s41467-023-39933-0","article-title":"Spatial cellular architecture predicts prognosis in glioblastoma","volume":"14","author":"Zheng","year":"2023","journal-title":"Nat Commun"},{"key":"2024100505384665500_ref79","doi-asserted-by":"publisher","first-page":"18802","DOI":"10.1038\/s41598-020-75708-z","article-title":"Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer","volume":"10","author":"Levy-Jurgenson","year":"2020","journal-title":"Sci Rep"},{"key":"2024100505384665500_ref80","doi-asserted-by":"publisher","first-page":"3877","DOI":"10.1038\/s41467-020-17678-4","article-title":"A deep learning model to predict RNA-seq expression of tumours from whole slide images","volume":"11","author":"Schmauch","year":"2020","journal-title":"Nat Commun"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae476\/59580854\/bbae476.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/6\/bbae476\/59580854\/bbae476.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T05:39:01Z","timestamp":1728106741000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae476\/7810615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,23]]},"references-count":80,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,9,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae476","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,11]]},"published":{"date-parts":[[2024,9,23]]},"article-number":"bbae476"}}