Stephanie Hicks -- Scalable statistical methods and software for single-cell data science
|Starts:||14:00 12 Jul 2021|
|Ends:||14:00 12 Jul 2021|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Join us for this seminar by Stephanie Hicks (Johns Hopkins, USA) as part of the North West Seminar Series in Mathematical Biology and Data Sciences. Details of the full series can be found here https://www.cms.livjm.ac.uk/APMSeminar/
Please contact firstname.lastname@example.org or email@example.com for the zoom link, or sign up to the mailing list.
Abstract: Single-cell RNA-Seq (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. However, single-cell data present unique challenges that have required the development of specialized methods and software infrastructure to successfully derive biological insights. Compared to bulk RNA-seq, there is an increased scale of the number of observations (or cells) that are measured and there is increased sparsity of the data, or fraction of observed zeros. Furthermore, as single-cell technologies mature, the increasing complexity and volume of data require fundamental changes in data access, management, and infrastructure alongside specialized methods to facilitate scalable analyses. I will discuss some challenges in the analysis of scRNA-seq data and present some solutions that we have made towards addressing these challenges.
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Role: Assistant Professor
Organisation: John Hopkins Bloomberg School of Public Health
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