Lecture 5: Gene Expression Data & Annotations (06/24/2024)

↩︎️ Lecture Schedule


What we will learn

Gene Expression Data

  • RNA-seq (and other methods) allow for globally studying gene expression to understand protein and drawing biological conclusions (treatment, prevention, and prognosis of diseases)

  • The basic workflow for NGS data analysis can be divided into three major parts:

          I. RNA-seq Library Prep & Sequencing                          II. Data Preprocessing                    III. Data Analysis    

  • Normalization is essential to make accurate comparisons of gene expression between samples and there are different ways to normalize the data depending on what concerns you have with the data

  • To get more information, annotations from database are used and can be merged with dplyr::*_join() functions gene expression

What we will Practice

Using a publicly-available RNA seq data (see overview here), we will perform each step in the data analysis workflow