Re: [AIRG] Generative models for single-cell RNA-seq, 10/03, CS 3310

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

Today, Matt will be taking us through work on probabilistic modeling of 
single-cell RNA sequencing. It involves neural networks, Bayesian 
modeling, and variational inference.

4pm, CS 3310
https://doi.org/10.1101/292037

I hope to see you there!

Aubrey


On 09/26/2018 06:43 PM, MATTHEW NATHAN BERNSTEIN via AIRG wrote:
> Hello AIRG,
> 
> Next week I will discuss an application of modern machine learning 
> methods in an exciting and emerging area of computational biology: 
> single-cell RNA sequencing analysis.
> 
> *Title*: Bayesian Inference for a Generative Model of Transcriptome 
> Profiles from Single-cell RNA Sequencing â
> *Authors*: Romain Lopez and others â
> *Paper*: https://doi.org/10.1101/292037â;
> *Presenter*: Matt Bernstein
> *âTime*: 4pm - Oct 3, 2018â
> *Place*: CS 3310
> 
> *Summary*:â
> Single-cell RNA-sequencing is an emerging technology that enables 
> scientists to measure gene expression across hundreds of thousands of 
> individual cells. These data pose numerous analysis challenges due to 
> the high dimensionality of the data, the numerous interactions between 
> genes, as well as the technical biases introduced in the experimental 
> process. This week, I will discuss a recent paper that addresses some 
> of these challenges by posing a novel probabilistic generative model of 
> single-cell RNA-seq data. This work integrates a number of machine 
> learning areas including neural networks, hierarchical Bayesian models, 
> and blackbox variational inference. Furthermore, this work provides a 
> case study for the application of recent machine learning methods to a 
> type of data that may be unfamiliar to pure machine learning and 
> computer science researchers.
> 
> 
> 
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