Shiny overview and LDA analysis of cancer genes

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# Introduction

This month, Alyssa Columbus will give an overview of the Shiny package. Shiny is an innovative package for R users to develop web applications. It makes it easier for R users to share results from their analyses visually.

We also are delighted that Zhi Yang will discuss the used of topic modelling in the analysis of cancer genetics. She will discuss the use of Latent Dirichlet Allocation (LDA) and its application in investigating cancer patients' mutational profiles.

Come early, network, enjoy the food and talks. Do not forget to purchase a raffle ticket or two. It helps support the meet-up and we have a great prize. A book of your choice from CRC Press. Yup, you get to chose the book!

# Schedule
6:30 - 6:50 Networking
6:50 - 7:00 Welcome & general announcement
7:00 - 7:30 Data Visualization with R Shiny
7:30 - 8:00 Topic modeling in cancer patients' mutational profiles
8:00 - Raffle
8:00 - 8:30 Networking and Clean-up

# Talk 1

## Title: Data Visualization with R Shiny
## Speaker: Alyssa Columbus
## Abstract

An overview of the key ideas that will help you build simple yet robust Shiny applications and walks you through building data visualizations using the R Shiny web framework. You’ll learn how to use R to prepare data, run simple analyses, and display the results in Shiny web applications as you get hands-on experience creating effective and efficient data visualizations. Along the way, Alyssa shares best practices to make these applications suitable for production deployment.

Topics include:

* R basics for data preparation, analysis, and visualization
* The structure of a Shiny app
* Interactive elements and reactivity
* Customizing the user interface with HTML and CSS
* Best practices for Shiny app production deployment
* Shiny dashboards, R Markdown, and Shiny app sharing

# Talk 2

## Title: Topic modeling in cancer patients' mutational profiles
## Speaker: Zhi Yang ([masked])
## Abstract:

Topic models allow us to access the contribution of each topic and its
representations across different documents. Human genomes have been
exposed to an assortment of mutational processes by contributing to
unique patterns of somatic mutations. What would happen if we apply
the same concept to the somatic mutations obtained from the cancer
patients and look for “topics” of mutations? I will introduce a simple
example of Latent Dirichlet Allocation (LDA) and its application in
investigating cancer patients' mutational profiles in addition to
available Bayesian tools in R to conduct statistical inference.