Error and Condition Handling and, LDA Analysis of Cancer Genes

# Introduction

This month, John Peach will give a talk on the basics of using the error handling system in R. He will also give a brief overview of the Condition Signalling system that it is based on. This is a powerful messaging system that is used for messages, warnings, errors and so much more.

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. If you purchase $5 worth you will also get $25 in AWS credits. It helps support the meet-up and we have a great prizes. This month we have video access to Data Science Go conference videos (a $149 value).

# Schedule
6:30 - 6:50 Networking
6:50 - 7:00 Welcome & general announcement
7:00 - 7:30 Error and Condition Handling in R
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: Error and Condition Handling in R
## Speaker: John Peach
## Abstract

R has an interesting system for handling messages, warnings and error conditions. This system is not standardised but is built on top of a powerful signalling system. In this talk we will go over the basics of how to send messages to the user and suppress them. We will also look and some of the more common options to control the behaviour and maybe a few that you have not heard of even if you have been using R for a while. We will also demonstrate the trycatch approach to dealing with errors. In addition we will go over the basics of the condition signalling system and give you some hints on how powerful a system this is.

# Talk 2

## Title: Topic modeling in cancer patients' mutational profiles
## Speaker: Zhi Yang (zyang895@gmail.com)
## 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.