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RNA-Seq On-Ramp

6-Week Cohort-Based Course

Runs October 4 to November 15

26 people enrolled

 

Make sense of any RNA-seq dataset with this guided computational experience. Designed by a biologist, for biologists.

Biology has been transformed by large-scale, high-dimensional data. And biologists are increasingly expected to have the computational skills to handle this data. But traditional life science training does not equip biologists with these skills. I created this course to address this glaring training gap, starting with RNA-seq data.

Why is RNA-seq data the first data type that biologists should learn to work with?

RNA-seq data is ubiquitous in the life sciences, from neuroscience to cancer biology to immunology. Biologists who learn how to analyze this high-dimensional data instantly unlock petabytes of data to better inform scientific decisions. They become more versatile and valuable scientists in both academic and industry settings.

This course teaches biologists how to make sense of any RNA-seq dataset.

"I would like to thank you Dean for such an interesting bioinformatics course. Although I did RNA-seq in the past, this really helped me shed some light on the mechanism of PCA and VST :) All this newly gained knowledge will definitely help me with my RNA-seq analyses during my PhD!"

B.B., PhD Student in Molecular Biology

"I also want to say this was a really fun exercise. The biological meaning that came through at the end really reinforced why I’m pursuing this field in the first place. Thank you for a great course and for all the time and effort you put in! I learned a ton."

J.H., Master's Student at Johns Hopkins University

"The course was extremely helpful for me in learning formal RNA-seq data analysis, since I had mostly been self-teaching computational skills before. I also really appreciated being part of a cohort and seeing how others approached the problems. Thank you again for creating such a valuable learning opportunity!"

M.O., Senior Scientist at New England Pharma

"I just took this course as a wet-lab scientist looking to gain more experience with R programming. Dean's explanations of complex concepts like principal component analysis and hierarchical clustering were very accessible to me and I appreciated his detail in answering questions in office hours. I would highly recommend!"

K.C., New York

What you'll learn

  • How to deal with common issues when preparing RNA-seq data for analysis
  • Principal component analysis
  • Hierarchical clustering
  • Differential expression analysis
  • How to frame a biological question as an actionable RNA-seq analysis
Principal Component Analysis
Hierarchical Clustering
Differential Expression Analysis
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What you'll get

  • 6 R notebooks of content with 2000+ lines of code, instructions, and exercises
  • Answer keys to every exercise
  • A coding environment in the cloud that requires zero troubleshooting. It just runs. See video below.
 
  • Demonstration of your skills through an independent project. See one student's example below!
See Example Project
  • Bonus Project: An additional set of R notebooks that challenge you to expand the scope and significance of your project.

How you'll grow professionally

  • No longer rely on others for RNA-seq data analysis
  • Know how to strengthen scientific decisions in your research with RNA-seq data
  • Add an in-demand computational skill to your resume
  • Become more competitive for grad schools, jobs, or promotions

Who this course is for

Biologists working in academia or industry - staff scientists, postdocs, research assistants, etc. - who want to demonstrate growth in computational skills and extend impact at work

Students in a life science PhD program who want to strengthen their thesis with a computational component and be more competitive for jobs after graduation

Students in a life science bachelor’s or master’s program who want be more competitive for research lab positions, grad schools, or jobs after graduation

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Course details

Prerequisites
  • At least one year of university-level biology
  • Familiarity with RStudio
  • Familiarity with data manipulation using base R and/or tidyverse
  • Familiarity with creating plots using ggplot2
  • Familiarity with ChatGPT
Time commitment
  • 6-12 hours per week, depends on how comfortable you are with R programming to begin with
Course schedule
  • Orientation (10/4)
  • Week 1: Data Cleaning (10/5 - 10/11)
  • Week 2: Data Normalization (10/12 - 10/18)
  • Week 3: Principal Component Analysis (10/19 - 10/25)
  • Week 4: Hierarchical Clustering (10/26 - 11/1)
  • Week 5: Differential Expression Analysis (11/2 - 11/8)
  • Week 6: Independent Project (11/9 - 11/15)
Weekly course format
  • You will work through one R notebook each week. All of the instructions along with exercises are presented in these notebooks directly.
  • A recorded code walk-through for each week's R notebook helps you get started.
  • Two mid-week office hours help you get unstuck.
  • An online community created for each cohort helps students connect with and help one another throughout the week.
  • Students share their code and a reflection at the end of each week with the community as an accountability measure.
Special features

Code walk-throughs: Weekly code walk-throughs to help you hit the ground running

Office hours: Weekly office hours to keep you on track

Community of peers: Stay accountable and share insights with like-minded professionals in a designated online community

Asynchronous delivery: Course is designed to fit into busy people's lives

Lifetime access: Revisit course content and R notebooks whenever you need to, even after the course officially ends

Refund guarantee: Students can request a full refund at any time up to the midpoint of the course, no questions asked

Enroll

Meet the instructor

Dean started his graduate training at Harvard and is currently a principal scientist in computational biology at Novartis. He has 8 years of experience as a computational biologist, spanning both academia and industry. Dean made the transition from 100% wet-lab to 100% dry-lab work, so he knows firsthand the many obstacles that prevent biologists from quickly learning computational skills.

Dean has 40,000+ followers on LinkedIn, 23,000+ subscribers to his LinkedIn newsletter, and 2,600+ downloads of his free course, Bare Minimum R. A primary focus of his LinkedIn content is how biologists can accelerate their learning of computational skills to adapt to a data-dominant future.

Dean has served on industry panels for Harvard, MIT, Carnegie Mellon, Columbia, UNC Chapel Hill, and CSU Long Beach career events. Dean was also an invited speaker, for both professional development and scientific talks, to the Festival of Genomics & Biodata.