Lecture 1: Introduction to RNA Sequencing: Quantifying Gene Expression

I gave two Guest Lectures for this class. The first lecture focused on how sequencing data is generated, and how to preprocess raw reads from a sequencer into a gene counts matrix.

Lecture 2: Introduction to RNA Sequencing: Analyzing Gene Expression

The second lecture focused on what to do with a counts matrix. The standard steps for analyzing the data include filtering and normalization, visualization, differential expression analysis, and modeling.

Practice Code for Lecture 2

I also made two practice Google Colab notebooks for the second lecture. The first notebook is a general introduction to working with large datasets:

Practice Working with Big Data

The second notebook uses DESeq2 to run a differential expression analysis on sequencing data:

Practice using DESeq2 for Differential Expression Analysis