{"id":1896,"date":"2022-05-25T22:42:38","date_gmt":"2022-05-25T22:42:38","guid":{"rendered":"https:\/\/eccb2022.org\/?page_id=1896"},"modified":"2022-07-01T07:51:38","modified_gmt":"2022-07-01T07:51:38","slug":"ntb-t04","status":"publish","type":"page","link":"https:\/\/eccb2022.org\/ntb-t04\/","title":{"rendered":"NTB-T04"},"content":{"rendered":"\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-6f6b038\" data-block-id=\"6f6b038\"><style>.stk-6f6b038 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>Title<\/strong><\/span><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-9a24379\" data-block-id=\"9a24379\"><h3 class=\"stk-block-heading__text\">Functional analysis of single-cell transcriptomics<\/h3><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-77074a6\" data-block-id=\"77074a6\"><style>.stk-77074a6 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #1a928e);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #1a928e);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Tutorial<\/span><\/span><\/span><\/strong><\/span> details<\/span><\/span><\/span><\/strong><\/span><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<ul><li>Date: Sunday, September 18th<\/li><li>Time: 09:00 to 13:00 CEST (Slot 22)<\/li><li>Format: Face-to-face<\/li><li>Room: TBD<\/li><\/ul>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-0994530\" data-block-id=\"0994530\"><style>.stk-0994530 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Instructors<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<ul><li><strong>Pau Badia i Mompel<\/strong>, PhD candidate, Saez-Rodrigez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany)<\/li><li><strong>Robin Browaeys<\/strong>, Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, (Belgium)<\/li><li><strong>Daniel Dimitrov<\/strong>, PhD Candidate, Saez-Rodriguez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany)<\/li><\/ul>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-d39beb2\" data-block-id=\"d39beb2\"><style>.stk-d39beb2 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Summary<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>Recent advances in omics technologies have led to a rapid increase in the popularity and applications of single-cell data-sets. Standard analyses and workflows solely focus on basic preprocessing steps followed by the identification of differentially expressed genes, and their subsequent use in cell-type annotation and characterization of biological processes. In this tutorial, we show how prior knowledge can be used to extend each of the aforementioned steps, as well as to extract clear biological insights. Furthermore, we provide an introduction to the state-of-the-art intercellular communication methods, as tools for systems-level hypothesis generation tools in single-cell data. We thus cover a diverse set of prior knowledge resources and show how these can be used to support and extend analysis steps ranging from quality control, cell-type annotation and transcription factor and cytokine activity inference. Finally, we show how advanced functional omics analyses can be used to refine cell-cell communication predictions.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-4a8e43e\" data-block-id=\"4a8e43e\"><style>.stk-4a8e43e .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><strong>Intended audience<\/strong><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>Broad audience with some experience in omics data-analysis and interest in analyzing single-cell transcriptomics.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading oculto stk-block-heading stk-block stk-4cdfd01\" data-block-id=\"4cdfd01\"><style>.stk-4cdfd01 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><strong>Prerequisites<\/strong><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<ul><li><\/li><\/ul>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-cdd954b\" data-block-id=\"cdd954b\"><style>.stk-cdd954b .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><strong>Maximum number of attendees<\/strong><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>30<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-1689a19\" data-block-id=\"1689a19\"><style>.stk-1689a19 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><strong><strong>Material required (for participants)<\/strong><\/strong><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>Laptop with at least 4GB of RAM memory and 10 GB of free disk space, with the package management system Conda installed.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-38c235b\" data-block-id=\"38c235b\"><style>.stk-38c235b .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><strong>Programme<\/strong><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-columns oculto stk-block-columns stk-block stk-e8ab19b\" data-block-id=\"e8ab19b\"><div class=\"stk-row stk-inner-blocks stk-block-content stk-content-align stk-e8ab19b-column\">\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-c5682ac\" data-block-id=\"c5682ac\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-c5682ac-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks\">\n<ul><li>Brief summary to single-cell transcriptomics pre-processing (25 min)<\/li><\/ul>\n\n\n\n<p>To start, we will briefly go over the basics of single-cell transcriptomics: reading raw data, QC filtering, normalization, feature selection, dimensionality reduction and visualization using the scanpy framework<\/p>\n\n\n\n<ul><li>Prior-knowledge-driven cell-type annotation (20 min)<\/li><\/ul>\n\n\n\n<p>We will introduce the concept of gene sets, and how to use them to aid in cell identity annotation using over-representation analysis and PanglaoDB, a database of cell type-specific marker genes.<\/p>\n\n\n\n<ul><li>Gene set enrichment of biological terms (20 min)<\/li><\/ul>\n\n\n\n<p>Then we will show different gene set data-base resources like MSigDB, and how they can be coupled to a statistical method like GSEA or over-representation analysis to infer enrichment of biological terms in scRNA-seq.<\/p>\n\n\n\n<ul><li>Transcription factor and cytokine activity estimation from prior-knowledge footprints (30 min)<\/li><\/ul>\n\n\n\n<p>Afterwards, we will introduce the concept of footprints and compare them to gene sets. As examples, we will show how to infer transcription factors and cytokine activities with the decoupleR framework, making use of the gene regulatory network DoRothEA and CytoSig\u2019s cytokine signatures, respectively.<\/p>\n\n\n\n<ul><li>Break (15 min)<\/li><\/ul>\n\n\n\n<ul><li>Cell-cell communication inference (30 min)<\/li><\/ul>\n\n\n\n<p>We will introduce the audience to the relevance, concepts, and assumptions behind intercellular communication inference. We will showcase these with a hands-on tutorial on ligand-receptor interaction prediction methods, including CellPhoneDB, NATMI, and SingleCellSignalR, via LIANA.<\/p>\n\n\n\n<ul><li>Modeling intercellular communication by linking ligands to target genes (25 minutes)<\/li><\/ul>\n\n\n\n<p>We will then show how one could go beyond ligand-receptor interactions by incorporating prior-knowledge of intracellular signalling. This part will largely focus on introducing the audience to the NicheNet method.<\/p>\n\n\n\n<ul><li>Connecting biological activities to intercellular communication (15 min)<\/li><\/ul>\n\n\n\n<p>Finally, we will summarize the lessons from the tutorial by connecting the results from different analyses, and thus highlighting their potential as complementary hypothesis-generation tools.<\/p>\n<\/div><\/div><\/div>\n<\/div><\/div>\n\n\n\n<figure class=\"wp-block-table tabla-ntb\" style=\"font-size:15px\"><table><thead><tr><th class=\"has-text-align-left\" data-align=\"left\"> <strong>T<\/strong>IME <\/th><th class=\"has-text-align-left\" data-align=\"left\"><strong>CONTENT<\/strong><\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>9:00 &#8211; 9:25<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Brief summary to single-cell transcriptomics pre-processing<\/strong><br>To start, we will briefly go over the basics of single-cell transcriptomics: reading raw data, QC filtering, normalization, feature selection, dimensionality reduction and visualization using the scanpy framework<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">9:25 &#8211; 9:45<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Prior-knowledge-driven cell-type annotation<\/strong><br>We will introduce the concept of gene sets, and how to use them to aid in cell identity annotation using over-representation analysis and PanglaoDB, a database of cell type-specific marker genes.<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">9:45 &#8211; 10:05<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Gene set enrichment of biological terms<\/strong><br>Then we will show different gene set data-base resources like MSigDB, and how they can be coupled to a statistical method like GSEA or over-representation analysis to infer enrichment of biological terms in scRNA-seq.<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>10:05 &#8211; 10:35<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Transcription factor and cytokine activity estimation from prior-knowledge footprints<\/strong><br>Afterwards, we will introduce the concept of footprints and compare them to gene sets. As examples, we will show how to infer transcription factors and cytokine activities with the decoupleR framework, making use of the gene regulatory network DoRothEA and CytoSig\u2019s cytokine signatures, respectively.<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Break<\/strong><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>10:50 &#8211; 11:20<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Cell-cell communication inference<\/strong><br>We will introduce the audience to the relevance, concepts, and assumptions behind intercellular communication inference. We will showcase these with a hands-on tutorial on ligand-receptor interaction prediction methods, including CellPhoneDB, NATMI, and SingleCellSignalR, via LIANA.<br><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span><span style=\"font-weight: bold; color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">11:20 &#8211; 11:45<\/span><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Modeling intercellular communication by linking ligands to target genes<\/strong><br>We will then show how one could go beyond ligand-receptor interactions by incorporating prior-knowledge of intracellular signalling. This part will largely focus on introducing the audience to the NicheNet method.<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">11:45 &#8211;  12:00<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Connecting biological activities to intercellular communication<\/strong><br>Finally, we will summarize the lessons from the tutorial by connecting the results from different analyses, and thus highlighting their potential as complementary hypothesis-generation tools.<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Title Functional analysis of single-cell transcriptomics Tutorial details Date: Sunday, September 18th Time: 09:00 to 13:00 CEST (Slot 22) Format: Face-to-face Room: TBD Instructors Pau Badia i Mompel, PhD candidate, Saez-Rodrigez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany) Robin Browaeys, Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, (Belgium) Daniel Dimitrov, PhD Candidate, Saez-Rodriguez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany) Summary Recent advances in omics technologies have led to a rapid increase in the popularity and applications of single-cell data-sets. Standard analyses and workflows solely focus on\u2026<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":4},"page_title_panel":"","has_hero_section":"default","7b9ded946d4680066060ddae606a4869":"","hero_section":"type-1","hero_elements":[{"id":"custom_title","enabled":true,"heading_tag":"h1","title":"Inicio"},{"id":"custom_description","enabled":true,"description_visibility":{"desktop":true,"tablet":true,"mobile":false}},{"id":"custom_meta","enabled":false,"meta_elements":[{"id":"author","enabled":true,"label":"Por","has_author_avatar":"yes","avatar_size":25},{"id":"post_date","enabled":true,"label":"El","date_format_source":"default","date_format":"M j, Y"},{"id":"updated_date","enabled":false,"label":"El","date_format_source":"default","date_format":"M j, 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0.04)"}},"boxed_content_spacing":{"desktop":{"top":"40px","bottom":"40px","left":"40px","right":"40px","linked":true},"tablet":{"top":"35px","bottom":"35px","left":"35px","right":"35px","linked":true},"mobile":{"top":"20px","bottom":"20px","left":"20px","right":"20px","linked":true}},"content_boxed_radius":{"top":"3px","bottom":"3px","left":"3px","right":"3px","linked":true},"c17a8304e5e66c0922c2d4f80257a4e2":"","disable_featured_image":"no","disable_share_box":"no","disable_header":"no","disable_footer":"no"},"featured_image_urls":{"full":"","thumbnail":"","medium":"","medium_large":"","large":"","1536x1536":"","2048x2048":""},"post_excerpt_stackable":"<p>Title Functional analysis of single-cell transcriptomics Tutorial details Date: Sunday, September 18thTime: 09:00 to 13:00 CEST (Slot 22)Format: Face-to-faceRoom: TBD Instructors Pau Badia i Mompel, PhD candidate, Saez-Rodrigez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany)Robin Browaeys, Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, (Belgium)Daniel Dimitrov, PhD Candidate, Saez-Rodriguez Group; Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg (Germany) Summary Recent advances in omics technologies have led to a rapid increase in the popularity and applications of single-cell data-sets. Standard analyses and workflows solely focus on basic preprocessing steps followed by&hellip;<\/p>\n","category_list":"","author_info":{"name":"victorcuencaharo","url":"https:\/\/eccb2022.org\/author\/victorcuencaharo\/"},"comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1896"}],"collection":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/comments?post=1896"}],"version-history":[{"count":19,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1896\/revisions"}],"predecessor-version":[{"id":2436,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1896\/revisions\/2436"}],"wp:attachment":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/media?parent=1896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}