{"id":13537,"date":"2023-08-21T16:19:49","date_gmt":"2023-08-21T14:19:49","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&#038;p=13537"},"modified":"2023-08-24T15:59:36","modified_gmt":"2023-08-24T13:59:36","slug":"67-spatially-aware-approach-for-cell-type-deconvolution-in-spatial-transcriptomics-data","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/67-spatially-aware-approach-for-cell-type-deconvolution-in-spatial-transcriptomics-data\/","title":{"rendered":"67. Spatially aware approach for cell-type deconvolution in spatial transcriptomics data"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Abstract<\/h2>\n\n\n<ul class=\"eplus-wrapper wp-block-list eplus-styles-uid-0183c8\"><\/ul>\n\n\n<p class=\" eplus-wrapper\">Spatial transcriptomics (ST) [2] and single-cell RNA-sequencing offer insights into the cell type topography in a tissue. Spots contain multiple cells, therefore the observed signal conveys information about mixtures of cells of different types, which allows researchers to create cell-type mapping models. One way of inference cell types decomposition is probabilistic graphical models are used to represent both hidden and visible variables in the model. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint distributions over numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graphs, and machine learning. The graphical model approach is added in the Celloscope [1] model. The Celloscope is a Bayesian model that estimates the abundance of cell types at each location by decomposing the spatial expression count matrix into a predefined set of reference cell-type signatures. The model takes an untranslated spatial expression count matrix of genes at localization and marker genes identification as input. In this presentation, I will talk about the modeling of cell decomposition problems based on spatial tran\u0002scriptomics and explain how they are modeled using probabilistic graphical models, in particular the Celloscope model and its simplification for my work. In my work, I consider the spatial interaction between the presence of types that collocate. I will talk about this idea introduced in my extension of Celloscope model assumptions.<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>References:<\/strong><\/p>\n\n\n<ul class=\"eplus-wrapper wp-block-list eplus-styles-uid-d43ab3\">\n<li class=\" eplus-wrapper\">[1] Agnieszka Geras et al. \u201cCelloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data\u201d. In: bioRxiv (2022). doi: 10.1101\/2022.05.24.493193. eprint: <a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf<\/a>\u00a0<br><a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https :\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193<\/a><\/li>\n\n\n\n<li class=\" eplus-wrapper\">[2] Vivien Marx. \u201cMethod of the Year: spatially resolved transcriptomics\u201d. eng. In: Nature methods 18.1 (2021), pp. 9\u201314. issn: 1548-7091.<\/li>\n<\/ul>\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author <\/h2>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Marcin Wierzbi\u0144ski<\/strong>&nbsp;is a Scientific Programmer at SANO, Center for Computational Medicine. He graduated bachelor\u2019s degree in 2020 from the Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw (MIMUW) with a related bachelor\u2019s thesis: \u201dSolving SAT problems using quantum algorithms\u201d. He gathered experience as a software engineer and research associate intern at the Polish Academy of Science \u2013 Centre for Theoretical Physics with experience in deep learning and quantum information. Currently, in his final year of mathematics with a specialization in Machine Learning at MIMUW. His master\u2019s thesis is on \u201dSpatially aware approach for cell-type deconvolution in spatial transcriptomics data\u201d. During his graduate studies, he spent a semester at the University of Oslo.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Marcin Wierzbi\u0144ski \u2013 Scientific Programmer at SANO, Center for Computational Medicine<\/p>\n","protected":false},"featured_media":13538,"template":"","class_list":["post-13537","seminars","type-seminars","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>67. Spatially aware approach for cell-type deconvolution in spatial transcriptomics data - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/seminars\/67-spatially-aware-approach-for-cell-type-deconvolution-in-spatial-transcriptomics-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"67. 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class=\" eplus-wrapper eplus-styles-uid-0183c8\"><\/ul>","innerContent":["\n<ul class=\" eplus-wrapper\"><\/ul>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fkdIjV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Spatial transcriptomics (ST) [2] and single-cell RNA-sequencing offer insights into the cell type topography in a tissue. Spots contain multiple cells, therefore the observed signal conveys information about mixtures of cells of different types, which allows researchers to create cell-type mapping models. One way of inference cell types decomposition is probabilistic graphical models are used to represent both hidden and visible variables in the model. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint distributions over numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graphs, and machine learning. The graphical model approach is added in the Celloscope [1] model. The Celloscope is a Bayesian model that estimates the abundance of cell types at each location by decomposing the spatial expression count matrix into a predefined set of reference cell-type signatures. The model takes an untranslated spatial expression count matrix of genes at localization and marker genes identification as input. In this presentation, I will talk about the modeling of cell decomposition problems based on spatial tran\u0002scriptomics and explain how they are modeled using probabilistic graphical models, in particular the Celloscope model and its simplification for my work. In my work, I consider the spatial interaction between the presence of types that collocate. I will talk about this idea introduced in my extension of Celloscope model assumptions.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Spatial transcriptomics (ST) [2] and single-cell RNA-sequencing offer insights into the cell type topography in a tissue. Spots contain multiple cells, therefore the observed signal conveys information about mixtures of cells of different types, which allows researchers to create cell-type mapping models. One way of inference cell types decomposition is probabilistic graphical models are used to represent both hidden and visible variables in the model. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint distributions over numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graphs, and machine learning. The graphical model approach is added in the Celloscope [1] model. The Celloscope is a Bayesian model that estimates the abundance of cell types at each location by decomposing the spatial expression count matrix into a predefined set of reference cell-type signatures. The model takes an untranslated spatial expression count matrix of genes at localization and marker genes identification as input. In this presentation, I will talk about the modeling of cell decomposition problems based on spatial tran\u0002scriptomics and explain how they are modeled using probabilistic graphical models, in particular the Celloscope model and its simplification for my work. In my work, I consider the spatial interaction between the presence of types that collocate. I will talk about this idea introduced in my extension of Celloscope model assumptions.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Ysscne","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>References:<\/strong><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>References:<\/strong><\/p>\n"]},{"blockName":"core\/list","attrs":{"epStylingOptions":{"columnsResponsiveEnabled":false,"columnsHoverEnabled":false,"itemsSpacingResponsiveEnabled":false,"itemsSpacingHoverEnabled":false,"listStyleResponsiveEnabled":false,"listStyleHoverEnabled":false,"listIconResponsiveEnabled":false,"listIconHoverEnabled":false,"columns":{"target":"","responsive":true,"hover":true,"options":[{"custom":true,"control":"ToggleOptions"},{"label":"Columns","control":"Range","attribute":"columns","css":"grid-template-columns","customValue":"repeat({{value}}, 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class=\" eplus-wrapper\">[1] Agnieszka Geras et al. \u201cCelloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data\u201d. In: bioRxiv (2022). doi: 10.1101\/2022.05.24.493193. eprint: <a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf<\/a>&nbsp;<br><a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https :\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193<\/a><\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">[1] Agnieszka Geras et al. \u201cCelloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data\u201d. In: bioRxiv (2022). doi: 10.1101\/2022.05.24.493193. eprint: <a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf<\/a>&nbsp;<br><a href=\"https:\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193.full.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https :\/\/www.biorxiv.org\/content\/early\/2022\/05\/25\/2022.05.24.493193<\/a><\/li>\n"]},{"blockName":"core\/list-item","attrs":{"epAnimationGeneratedClass":"edplus_anim-OvHy5u","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<li class=\" eplus-wrapper\">[2] Vivien Marx. \u201cMethod of the Year: spatially resolved transcriptomics\u201d. eng. In: Nature methods 18.1 (2021), pp. 9\u201314. issn: 1548-7091.<\/li>\n","innerContent":["\n<li class=\" eplus-wrapper\">[2] Vivien Marx. \u201cMethod of the Year: spatially resolved transcriptomics\u201d. eng. In: Nature methods 18.1 (2021), pp. 9\u201314. issn: 1548-7091.<\/li>\n"]}],"innerHTML":"<ul class=\" eplus-wrapper eplus-styles-uid-d43ab3\">\n\n<\/ul>","innerContent":["\n<ul class=\" eplus-wrapper\">",null,"\n\n",null,"<\/ul>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-xDxZla","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-CsrpxB","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author <\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">About the author <\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-vjYcpu","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Marcin Wierzbi\u0144ski<\/strong>&nbsp;is a Scientific Programmer at SANO, Center for Computational Medicine. He graduated bachelor\u2019s degree in 2020 from the Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw (MIMUW) with a related bachelor\u2019s thesis: \u201dSolving SAT problems using quantum algorithms\u201d. He gathered experience as a software engineer and research associate intern at the Polish Academy of Science \u2013 Centre for Theoretical Physics with experience in deep learning and quantum information. Currently, in his final year of mathematics with a specialization in Machine Learning at MIMUW. His master\u2019s thesis is on \u201dSpatially aware approach for cell-type deconvolution in spatial transcriptomics data\u201d. During his graduate studies, he spent a semester at the University of Oslo.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Marcin Wierzbi\u0144ski<\/strong>&nbsp;is a Scientific Programmer at SANO, Center for Computational Medicine. He graduated bachelor\u2019s degree in 2020 from the Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw (MIMUW) with a related bachelor\u2019s thesis: \u201dSolving SAT problems using quantum algorithms\u201d. He gathered experience as a software engineer and research associate intern at the Polish Academy of Science \u2013 Centre for Theoretical Physics with experience in deep learning and quantum information. Currently, in his final year of mathematics with a specialization in Machine Learning at MIMUW. His master\u2019s thesis is on \u201dSpatially aware approach for cell-type deconvolution in spatial transcriptomics data\u201d. During his graduate studies, he spent a semester at the University of Oslo.<\/p>\n"]}],"meta_data":{"event_day":"2022-06-20","event_time":"2:00-3:30 PM (CEST)","event_guest":"Marcin Wierzbi\u0144ski \u2013 Scientific Programmer at SANO, Center for Computational Medicine","has_medias":true,"medias":[{"icon":{"ID":1144,"id":1144,"title":"clock","filename":"clock.svg","filesize":1479,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/clock\/","alt":"clock Sano Seminar","author":"7","description":"","caption":"Sano Seminar clock","name":"clock","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:24:42","modified":"2024-10-09 16:41:04","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":56,"height":57,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","thumbnail-width":147,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium-width":294,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium_large-width":768,"medium_large-height":783,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","large-width":1004,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","1536x1536-width":56,"1536x1536-height":57,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","2048x2048-width":56,"2048x2048-height":57}},"title":"20th June 2022, 2:00-3:30 PM (CEST)","link":""},{"icon":{"ID":1146,"id":1146,"title":"camera","filename":"camera.svg","filesize":1129,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/camera\/","alt":"camera Sano Seminar","author":"7","description":"","caption":"Sano Seminar camera","name":"camera","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:25:24","modified":"2024-10-09 16:42:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":60,"height":38,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","thumbnail-width":150,"thumbnail-height":95,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium-width":300,"medium-height":190,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium_large-width":768,"medium_large-height":486,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","large-width":1024,"large-height":648,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","1536x1536-width":60,"1536x1536-height":38,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","2048x2048-width":60,"2048x2048-height":38}},"title":"Join via ZOOM on","link":{"title":"seminar.sano.science","url":"http:\/\/seminar.sano.science","target":"_blank"}}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13537","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/seminars"}],"version-history":[{"count":4,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13537\/revisions"}],"predecessor-version":[{"id":13584,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/13537\/revisions\/13584"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/13538"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=13537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}