{"id":16,"date":"2023-01-20T13:57:28","date_gmt":"2023-01-20T12:57:28","guid":{"rendered":"https:\/\/www.netmap.ai\/?page_id=16"},"modified":"2025-07-29T12:58:40","modified_gmt":"2025-07-29T10:58:40","slug":"netmap","status":"publish","type":"page","link":"https:\/\/www.netmap.ai\/","title":{"rendered":"About NetMap"},"content":{"rendered":"

NetMap is a BMFTR-funded research project run by FAU (<\/a>Biomedical Network Science Lab<\/a>)<\/a>, UHH (<\/a>Institute for Computational Systems Biology<\/a>)<\/a>, and TUM (<\/a>Chair of Animal Physiology and Immunology<\/a>)<\/a>.<\/p>\n

Motivation<\/strong><\/h3>\n

Thanks to rapid advances in single-cell RNA sequencing (scRNA-seq) technologies, it is now possible to quantify genome-wide gene expression profiles at single-cell resolution at affordable costs. scRNA-seq data hold enormous potential to decipher molecular mechanisms that modulate progression and treatment response in complex diseases such as cancer and to inform personalized treatment regimens. Since scRNA-seq data are extremely sparse and high-dimensional, reducing their dimensionality is a necessary preprocessing step for all analyses. Yet, when applied to scRNA-seq data, standard dimensionality reduction techniques have been shown to yield highly non-canonical and unstable results with unforeseeable effects on downstream analyses.<\/p>\n

Mission<\/strong><\/h3>\n

In NetMap, we will therefore integrate dimensionality reduction for scRNA-seq data with a central task in the detection of molecular disease mechanisms: the identification of key gene regulatory networks and transcription factors that drive cell differentiation. The new computational methods will be applied on scRNA-seq data for exhausted and non-exhausted CD4 T cells, where we will aim at deriving hypotheses on gene regulatory programmes driving T cell exhaustion. The most promising hypotheses will then be tested in pre-clinical in vivo<\/em> studies.<\/p>\n

NetMap kick-off<\/a><\/h2><\/header>
<\/div>
<\/div>
20\/01\/2023<\/div>
News<\/a><\/div><\/div>
The kick-off meeting for our project will take place in Erlangen on May 4\u20135, 2023.<\/div><\/article><\/section>\n","protected":false},"excerpt":{"rendered":"

NetMap is a BMFTR-funded research project run by FAU (Biomedical Network Science Lab), UHH (Institute for Computational Systems Biology), and TUM (Chair of Animal Physiology and Immunology). Motivation Thanks to… <\/a><\/p>\n","protected":false},"author":3370,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"_rrze_cache":"enabled","footnotes":""},"page_category":[],"page_tag":[],"class_list":["post-16","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/pages\/16","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/users\/3370"}],"replies":[{"embeddable":true,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16"}],"version-history":[{"count":10,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/pages\/16\/revisions"}],"predecessor-version":[{"id":115,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=\/wp\/v2\/pages\/16\/revisions\/115"}],"wp:attachment":[{"href":"https:\/\/www.netmap.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16"}],"wp:term":[{"taxonomy":"page_category","embeddable":true,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fpage_category&post=16"},{"taxonomy":"page_tag","embeddable":true,"href":"https:\/\/www.netmap.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fpage_tag&post=16"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}