[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125566-en":3,"doc-seo-125566-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125566,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data","Development and homeostasis in multicellular systems depend on precise spatial control of molecular pattern formation and maintenance. Spatially resolved, high-throughput imaging and sequencing approaches, especially spatial transcriptomics, generate large and complex datasets that have accelerated rapid development of deep learning–driven machine learning tools. These tools are increasingly embedded in integrated experimental and computational workflows to separate signal from noise, yet assumptions and methodologies can be hard to balance and interpret. This work summarizes key spatial transcriptomics analysis goals, current trends, and four data science concepts to guide tool selection for biological questions.","Machine Learning for Uncovering Biological Insights in Spatial  \nTranscriptomics Data  \nAlex J. Lee1*, Robert Cahill1*, Reza Abbasi-Asl1\\#  \n1 University of California, San Francisco  \n*These authors contributed equally to this work.  \n\\#Corresponding author: Reza Abbasi-Asl (Email: [Reza.AbbasiAsl@ucsf.edu](Reza.AbbasiAsl@ucsf.edu))  \nAbstract  \nDevelopment and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.  \nParallel advances in spatial transcriptomics and machine learning  \nThe parallel and rapid technology advances in both spatial transcriptomics (ST) and machine learning (ML) present an unprecedented opportunity to enhance our understanding of biology of cells, tissues, development and disease. Researchers can now identify spatial distributions of specific cell-types and the variability of specific gene of interest with in-situ hybridization techniques such as MERFISH 1 and smFISH2,3 . Next-generation sequencing based techniques such as Visium4 , GeoMx5 , and XYZeq6 allow unbiased spatial characterization of the whole transcriptome.  \nTechnology development in ST is rapid, and advances are expected to continue across three dimensions: higher resolution, integration with other data modalities, and robust quantification ofspatiotemporal dynamics7,8 . Rapid advances in ML are allowing biological researchers to capitalize on the increasingly large and complex ST datasets to facilitate biological discovery. Recently, ML’s deep learning subfield has opened new doors in biology, most notably AlphaFold’s dramatic impact on previously intractable problems in structural biology9–12. Methods leveraging convolutional, graph, and transformer-based neural networks are now amongst the state-of-the-art in areas as diverse as histology, multiplexed imaging, and gene and protein interaction network analysis 13–17.  \nHere, we offer a practical discussion of opportunities, trade-offs, and pitfalls in the ML-based analysis of spatial transcriptomics data and integration with other datasets. In order to facilitate clear descriptions of the trade-offs, we identify four relevant concepts in biological data science (accuracy, interpretability, stability, and computability)18,19 , and apply these concepts to ML for ST. Here, we aim to describe the ST questions enabled by ML, the key ML techniques and toolboxes for ST, and guidance for developing and applying these techniques based on the four principles of biological data science.  \nMachine learning as a fundamental tool for spatial transcriptomics data analysis  \nThere has been a profusion of new ML tools for ST, with dozens of ML tools now publicly available and new methods published on a monthly basis. We describe three categories of biological questions in ST data that can be investigated more effectively using ML and note a broad, but nonexhaustive list","cbCailObiqzlQqBc","https://ap.wps.com/l/cbCailObiqzlQqBc","pdf",1261879,1,11,"English","en",105,"# Abstract\n# Parallel Advances in Spatial Transcriptomics and Machine Learning\n## Technology Development in Spatial Transcriptomics\n## Rapid Advances in Machine Learning\n# Machine Learning as a Fundamental Tool for Spatial Transcriptomics Data Analysis\n## Critical Questions Addressable by Machine Learning\n## ML for Spatial Cell-Type Characterization","[{\"question\":\"What types of spatial transcriptomics datasets motivate machine learning approaches?\",\"answer\":\"Spatial transcriptomics and related imaging/assay technologies produce large, complex, spatially resolved measurements, including multiplexed immunofluorescence and whole-transcriptome profiling.\"},{\"question\":\"What are the four biological data science concepts used to guide ML tool choice?\",\"answer\":\"The document identifies accuracy, interpretability, stability, and computability as four relevant concepts for guiding analysis decisions.\"},{\"question\":\"How does machine learning help with spatial cell-type characterization in spatial transcriptomics?\",\"answer\":\"ML can use spatial information to enrich understanding of cell-type diversity and higher-order tissue structure, including approaches that transfer labels from annotated single-cell RNA-seq references and use projection/similarity mappings.\"}]","Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data | 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types of spatial transcriptomics datasets motivate machine learning approaches?","Question",{"text":75,"@type":76},"Spatial transcriptomics and related imaging/assay technologies produce large, complex, spatially resolved measurements, including multiplexed immunofluorescence and whole-transcriptome profiling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the four biological data science concepts used to guide ML tool choice?",{"text":80,"@type":76},"The document identifies accuracy, interpretability, stability, and computability as four relevant concepts for guiding analysis decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning help with spatial cell-type characterization in spatial transcriptomics?",{"text":84,"@type":76},"ML can use spatial information to enrich understanding of cell-type diversity and higher-order tissue structure, including approaches that transfer labels from annotated single-cell RNA-seq references and use 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