{"id":1296,"date":"2016-12-15T13:06:54","date_gmt":"2016-12-15T18:06:54","guid":{"rendered":"http:\/\/my.vanderbilt.edu\/irishlab\/?p=1296"},"modified":"2016-12-15T13:06:54","modified_gmt":"2016-12-15T18:06:54","slug":"webinar-kirsten-diggins-for-expert-cytometry-excyte","status":"publish","type":"post","link":"https:\/\/www.irishlab.org\/index.php\/2016\/12\/15\/webinar-kirsten-diggins-for-expert-cytometry-excyte\/","title":{"rendered":"Webinar: Kirsten Diggins for Expert Cytometry (Excyte)"},"content":{"rendered":"<figure id=\"attachment_1298\" aria-describedby=\"caption-attachment-1298\" style=\"width: 432px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1298\" src=\"http:\/\/localhost\/irishv01\/wp-content\/uploads\/2016\/12\/Excyte-diggins.jpg\" alt=\"2016 Diggins Excyte slide\" width=\"432\" height=\"121\" srcset=\"https:\/\/www.irishlab.org\/wp-content\/uploads\/2016\/12\/Excyte-diggins.jpg 432w, https:\/\/www.irishlab.org\/wp-content\/uploads\/2016\/12\/Excyte-diggins-300x84.jpg 300w\" sizes=\"auto, (max-width: 432px) 100vw, 432px\" \/><figcaption id=\"caption-attachment-1298\" class=\"wp-caption-text\">2016 Diggins Excyte slide<\/figcaption><\/figure>\n<p style=\"text-align: justify\"><!--more-->Today Kirsten Diggins presented a webinar for Expert Cytometry entitled, &#8220;Identifying and characterizing cell subpopulations from high dimensional\u00a0flow cytometry data.&#8221;<\/p>\n<p style=\"text-align: justify\">Webinar highlights:<\/p>\n<p style=\"text-align: justify\">Mass cytometry (CyTOF) and multiplexed fluorescence flow cytometry now allow us to measure\u00a0upwards of 40 features simultaneously at the single cell level. However, traditional analysis methods like\u00a0biaxial gating are often insufficient to extract meaningful information from this data. Here, we present a\u00a0modular workflow for high dimensional flow cytometry data analysis that maximizes the discovery of\u00a0small or rare cell subsets and provides a population-level view of expression signatures.<\/p>\n<p style=\"text-align: justify\">1. High quality results come from high quality data. Learn more about the steps involved in data\u00a0cleanup and pre-processing, including scale transformations, bead normalization, and cleanup\u00a0gating.<br \/>\n2. Computational tools for clustering, dimensionality reduction, and visualization are used to identify and characterize populations of cells in high dimensional data. Discover how these tools\u00a0can be combined to automatically identify cell populations and visualize their marker expression\u00a0profiles.<br \/>\n3. Before applying clustering methods to your data, it can be useful to identify and separate major populations of cells (i.e. CD45 high and low). Learn how to use t-distributed stochastic neighbor\u00a0embedding (tSNE) for dimensionality reduction and major population gating.<br \/>\n4. Cell populations can be automatically identified using cluster analysis. Learn how to use<br \/>\nspanning-tree progression analysis of density-normalized events (SPADE) to automatically identify populations of cells in your data.<br \/>\n5. Heatmaps of population-level median expression values are useful for interpreting results and generating new hypotheses. Discover how to export cell cluster information from Cytobank, load the data into R, and build heatmaps of expression signatures.<\/p>\n<p style=\"text-align: justify\">For more information, see Kirsten&#8217;s work in Methods and Nature Methods:<\/p>\n<p style=\"text-align: justify\"><a href=\"http:\/\/my.vanderbilt.edu\/irishlab\/2015\/06\/published-diggins-et-al-methods-2015\/\">Published: Diggins et al., Methods 2015<\/a><\/p>\n<p style=\"text-align: justify\"><a href=\"http:\/\/my.vanderbilt.edu\/irishlab\/2017\/03\/diggins-et-al-nature-methods-2017\/\">Published: Diggins et al., Nature Methods 2017<\/a><\/p>\n<p style=\"text-align: justify\"><a href=\"http:\/\/my.vanderbilt.edu\/irishlab\/2018\/01\/published-2018-diggins-et-al-current-protocols-in-cytometry\/\">Published: Diggins et al., Current Protocols in Cytometry 2018<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-1296","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/posts\/1296","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/comments?post=1296"}],"version-history":[{"count":0,"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/posts\/1296\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/media?parent=1296"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/categories?post=1296"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.irishlab.org\/index.php\/wp-json\/wp\/v2\/tags?post=1296"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}