Data Science Training Program
July 20-July 21, 2020

Krishna Choudhary, PhD

Gladstone Bioinformatics Core

RNA-Seq is a powerful tool to interrogate cellular functions. This intermediate workshop will teach you the skills you need to get the most out of your RNA-Seq data through analysis in R.

By the end of the workshop, you will know how to:

  • Go from a matrix of raw gene expression counts to differentially expressed genes. 
  • Analyze experimental designs that go beyond two-group comparisons using edgeR’s generalized linear modeling capabilities. 
  • Test specific hypotheses using a joint model fit.

This workshop is intended for an audience with 4 to 6 months experience programming in R and who have previously completed the introductory RNA-Seq Analysis in R workshop, or an equivalent.

Visit the workshop site to download materials.

Note: This workshop will be held over 2 days, July 20–21, 2020. Attendance in the first session is mandatory in order to attend the second session.

View upcoming workshops in the Gladstone Bioinformatics Program.

Details

Dates
July 20-July 21, 2020
Time
2:00-4:00pm PDT
Location
Online 2-Day Webinar
Audience
Gladstone and UCSF
Contact(s)





The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.

Diversity, Equity, and Inclusion

At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.

We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.