Overview

Transcriptomics 2: Statistical Analysis

Published

18 September, 2024

This week we cover differential expression analysis on raw counts or log normalised values. The independent study will allow you to check you have what you should have following the Transcriptomics 1: Hello Data workshop and Consolidation study. It will also summarise the concepts and methods we will use in the workshop. In the workshop, you will learn how to perform differential expression analysis on raw counts using DESeq2 (Love, Huber, and Anders 2014) or on logged normalised expression values using scran (Lun, McCarthy, and Marioni 2016) or both.

We suggest you sit together with your group in the workshop.

Learning objectives

The successful student will be able to:

  • verify they have the required RStudio Project set up and the data and code files from the previous Workshop and Consolidation study
  • explain the goal of differential expression analysis and the importance of normalisation
  • explain why and how the nature of the input values determines the analysis package used
  • describe the metadata needed to carry out differential expression analysis and the statistical models used by DESeq2 and scran
  • find genes that are unexpressed or expressed in a a single cell type or treatment group
  • perform differential expression analysis on raw counts using DESeq2 or on logged normalised expression values using scran or both.
  • explain the output of differential expression: log fold change, p-value, adjusted p-value,

Instructions

  1. Prepare

    1. πŸ“– Read what you should have so far and about concepts in differential expression analysis.

  2. Workshop

    1. πŸ’» Find unexpressed genes and those expressed in a single cell type or treatment group.

    2. πŸ’» Set up the metadata for differential expression analysis.

    3. πŸ’» Perform differential expression analysis on raw counts using DESeq2 or on logged normalised expression values using scran or both.

    4. Look after future you!

  3. Consolidate

    1. πŸ’» Use the work you completed in the workshop as a template to apply to a new case.

References

Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. β€œModerated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15: 550. https://doi.org/10.1186/s13059-014-0550-8.
Lun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. β€œA Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor.” F1000Res. 5: 2122. https://doi.org/10.12688/f1000research.9501.2.