{"id":191642,"date":"2014-11-06T00:00:00","date_gmt":"2014-11-06T09:19:28","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mapping-single-cells-a-geometric-approach\/"},"modified":"2016-07-15T15:20:48","modified_gmt":"2016-07-15T22:20:48","slug":"mapping-single-cells-a-geometric-approach","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/mapping-single-cells-a-geometric-approach\/","title":{"rendered":"Mapping Single Cells: A Geometric Approach"},"content":{"rendered":"<div class=\"asset-content\">\n<p>High dimensional single cell technologies are on the rise, rapidly increasing in accuracy and throughput.  These offer computational biology both a challenge and an opportunity.  One of the big challenges with this data-type is to understand regions of density in this multi-dimensional space, given millions of noisy measurements.  Underlying many of our approaches is mapping this high-dimensional geometry into a nearest neighbor graph and characterization single cell behavior using this graph structure.  We will discuss a number of approaches (1) An algorithm that harnesses the nearest neighbor graph to order cells according to their developmental maturity and its use to identify novel progenitor B-cell sub-populations.  (2) Using reweighted density estimation to characterize cellular signal processing in T-cell activation.  (2) New clustering and dimensionality reduction approaches to map heterogeneity between cells; with an application to characterizing tumor heterogeneity in Acute Myeloid Leukemia.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>High dimensional single cell technologies are on the rise, rapidly increasing in accuracy and throughput. These offer computational biology both a challenge and an opportunity. One of the big challenges with this data-type is to understand regions of density in this multi-dimensional space, given millions of noisy measurements. Underlying many of our approaches is mapping [&hellip;]<\/p>\n","protected":false},"featured_media":198723,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-191642","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/kctdEV8AoVQ","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/191642"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/191642\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/198723"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=191642"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=191642"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=191642"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=191642"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=191642"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=191642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}