Learning Gene Association Networks Using Single-Cell RNA-Seq Data: A Graphical Model Approach

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=QzhB9sx8AXY


Duration: 34:15
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Davide Risso (University of Padova)
Learning Gene Association Networks Using Single-Cell RNA-Seq Data: A Graphical Model Approach
Computational Challenges in Very Large-Scale 'Omics'

I will present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on a graphical model for count data. I will explore the use of different node-conditional distributions, including Poisson, negative binomial, and zero-inflated negative binomial, and discuss the advantages of each. I will show with simulations that our approach is able to retrieve the structure of a graph in a variety of settings and I will show the utility of the approach on two real datasets in the context of stem cell differentiation and response to treatment in cancer.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Computational Challenges in Very Large-Scale 'Omics'
Davide Risso