GeneTonic: enjoying the interpretation of your RNA-seq data analysis

Authors:
Federico Marini^[marinif@uni-mainz.de], Annekathrin Ludt^[anneludt@uni-mainz.de]
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz.
Last modified: 3 Aug, 2021.

Overview

Description

This workshop demonstrates the use of the GeneTonic package to integrate and explore the results of RNA-seq experiments, in the context of differential expression and functional enrichment analyses.

This will be proposed as a lab session that combines an instructor-led live demo, followed by hands-on experimentation guided by exercises, hints, and solutions that participants may continue to use after the workshop.

The instructor-led live demo comprises three parts:

1. Overview of the RNA-seq differential expression workflow.
2. Introduction to the GeneTonic package and its functionality.
3. Hands-on exercises and discussion.

Participants are encouraged to ask questions at any time during the workshop.

Pre-requisites

• Basic knowledge of RNA-seq analysis workflow
• Familiarity with concepts proper of differential expression analysis (e.g. in the DESeq2 framework, https://bioconductor.org/packages/DESeq2)
• Familiarity with functional enrichment analysis concepts (e.g. with the clusterProfiler package, or using the topGO wrapper included in the pcaExplorer package)

We recommend to use the latest version of R (>= 4.0.0) and the latest release of Bioconductor version (3.13).

Install the GeneTonic package

BiocManager::install("GeneTonic")
# alternatively, the development version directly from GitHub
BiocManager::install("federicomarini/GeneTonic")

Participation

Attendees will participate by following along a presentation introducing the GeneTonic package, RMarkdown documents which describe the tasks to perform, trying variations of provided code, and asking questions throughout the workshop.

R / Bioconductor packages used

• GeneTonic
• DESeq2

Time outline

Activity	Time
Overview	10m
Introduction to GeneTonic	25m
Q&As	10m

Workshop goals and objectives

Learning goals

• Integrate the different components from the Differential Expression analysis workflow
• Utilize interactive web applications to efficiently extract information of the combined input objects
• Adopt means to generate reproducible reports to capture the results of the live exploration

Learning objectives

• Setup a local environment to run GeneTonic on the results of own RNA-seq experiments
• Interact with the core components of GeneTonic to inspect the provided datasets
• Create a variety of interactive visualizations to summarize and help interpret the data at hand
• Practice the combination of interactivity and reproducibility to combine the advantages of these aspects in a single computational workflow