CBW Pathways Workshop
Pathway and Network Analysis of -Omics Data ( May 2021 )
Meet your Faculty
Gary Bader
Robin Haw
Michael Hoffman
Veronique Voisin
Ruth Isserlin
Welcome
Class Materials
Workshop Schedule
Pre-Workshop Materials and Laptop Setup Instructions
Laptop Setup Instructions
Basic programs
Java 11 Installation
Cytoscape Installation
GSEA Installation
Pre-workshop Tutorials
Cytoscape Preparation tutorials
Pre-workshop Readings and Lectures
Additional tutorials
Module 1 - Introduction to Pathway and Network Analysis (Gary Bader)
Module 2: Finding Over-represented Pathways (Veronique Voisin)
Module 2 lab - g:Profiler
Introduction
Goal of the exercise 1
Data
Exercise 1 - run g:Profiler
Step 1 - Launch g:Profiler.
Step 2 - input query
Step 3 - Adjust parameters.
Step 4 - Run query
Step 5 - Explore the results.
Step 6: Expand the stats tab
Step 7: Save the results
Step 8 (Optional but recommended)
Exercise 2: Load and use a custom .gmt file and run the query
Optional steps
Optional 1
:
Option 2
:
Option 3
:
Module 2 lab - GSEA
Introduction
Goal of the exercise
Data
Background
How to generate a rank file.
Start the exercise
Step1.
Step 2.
Step3.
Step 4.
Step 5.
Additional information
Module 3: Network Visualization and Analysis with Cytoscape
Module 3 Lab: g:profiler Visualization
Goal of the exercise
Data
EnrichmentMap
Description of this exercise
Start the exercise
Exercise 1a - compare different gprofiler geneset size results
Step 1
Step 2
Step3: Explore the results:
Explore Detailed results
Exercise 1b - Is specifying the gmt file important?
Exercise 1c - create EM from results using Baderlab genesets
Exercise 1c (optional) - investigate individual pathways in GeneMANIA or String
GeneMANIA
String
Module 3 Lab: GSEA Visualization
Goal of the exercise:
Data
EnrichmentMap
Exercise 1 - GSEA output and EnrichmentMap
Step 1
Step 2
Step 3
Step 4
Exercise 2 - Post analysis (add drug target gene-sets to the network)
Step 5
Exercise 3 - Autoannotate the Network
Step 6
Exercise 4 (Optional) - Explore results in GeneMANIA or STRING
Step 7
Module 3 Lab: (Bonus) Automation
Goal of the exercise:
Set Up - Option 1 - Install R/Rstudio
Set Up - Option 2 - Docker image with R/Rstudio
What is docker?
Docker - Basic term definition
Container
Image
Docker Volumes
Install Docker
Windows
MacOS / Linux
Create your first notebook using Docker
Start coding!
Start using automation
Step 1 - launch RStudio
Step 2 - create a new project
Step 3 - Open example up RNotebook
Step 4 - Define Notebook parameters
Step 5 - Step through notebook to run the analysis
Exercises
Additional resources
Module 4: In-depth Analysis of Networks and Pathways
Module 5: Regulatory Network Analysis
Lecture
Practical lab 1: chIP_seq data - GREAT and MEME-chIP
Practical lab 2: gene list - iREgulon and enrichr/EnrichmentMap
Additional slides about the tools Segway and BEHST presented during the lecture
Module 5 Lab 1: Gene Regulation and Motif Analysis Practical Lab /chIP-seq
Goal of this practical lab
Dataset used during this practical lab
Exercise 1 - Run pathway analysis using GREAT
Perform pathway enrichment
Explore the results.
Perform pathway enrichment - Proximal approach
Explore the results. - proximal analysis
Exercise 2 - Build an enrichment map to visualize GREAT results
Exercise 3 (optional): Practice building enrichment maps and auto-annotation
Optional exercise 3a: AutoAnnotate the enrichment map:
Optional exercise 3b: Repeat the process of building an enrichment map using the proximal data (Proximal_GOBP_greatExportAll.tsv).
Optional exercise 3c: Repeat the process by building both the Proximal and Distal enrichment maps at the same time.
Exercise 4: Add RUNX1 targets and RUNX1 KO genes on the distal enrichment map.
step 4a: post analysis:
Step 4b Optional: Change the edge style of the signature gene-sets:
Exercise 5: Learning how to run MEME-chip from the MEME suite (
https://meme-suite.org/meme/tools/meme-chip
)
Format the Data
Exercise 5a: Download sequences from .bed coordinates
Exercise 5b: Run MEME-chIP
Exercise 6 (optional): Get the iRegulon RUNX1 targets and find the mouse orthologs using g:Orth (from g:Profiler) to create the gmt file used in Exercise 4.
End of Lab
Module 5 Lab 2: Gene Regulation and Motif Analysis Practical Lab / iRegulon
iRegulon lab
Goal
Exercise 1. Detect regulons from co-expressed genes
Skills learned in this exercise:
Steps
Exercise 2. Create a metatargetome using iRegulon and merge 2 networks in Cytoscape.
Steps
Use or precomputed iRegulon results:
Notes about iRegulon:
Notes about Cytoscape:
Exercise 3. Use Enrichr with the prostate gene list.
Goal
Steps
end of practical lab
Module 6: Gene Function Prediction
Module 6 Lab: GeneMANIA (Cytoscape version)
Goal of this practical lab
EXERCISE 1: QUESTIONS AND STEPS TO FOLLOW
STEPS
EXERCISE 1 ANSWERS
EXERCISE 2: QUESTIONS AND STEPS TO FOLLOW
STEPS
EXERCISE 3: QUESTIONS AND STEPS TO FOLLOW
STEPS
SOME DEFINITIONS:
Module 6 Lab: GeneMANIA (web version)
Goal of this practical lab
EXERCISE 1: QUESTIONS AND STEPS TO FOLLOW
STEPS
EXERCISE 1 ANSWERS: DETAILED EXPLANATION AND SCREENSHOTS
EXERCISE 1 - STEPS 1-4
EXERCISE 1 - STEP 5
EXERCISE 1 - STEP 6
Exercise 1 - STEP 7
Exercise 1 - STEP 8
Exercise 1 - STEP 9
Exercise 1 - STEP 10 (layouts)
Exercise 1 - STEP 11 (save an image)
EXERCISE 2: QUESTIONS AND STEPS TO FOLLOW
STEPS
EXERCISE 2 ANSWERS: DETAILED STEPS AND SCREENSHOTS
Exercise 2 - STEPS 1 to 4
Exercise 2 - STEP 5
Exercise 2 - STEP 6.
Exercise 2 - STEP 7
Exercise 2 - STEP 8
Exercise 2 - STEP 9
Exercise 2 - STEP 10
Exercise 2 - STEP 11
Exercise 2 - STEP 12
Exercise 2 - STEP 13.
EXERCISE 3: QUESTIONS AND STEPS TO FOLLOW
STEPS
Exercise 3: MORE DETAILS AND SCREENSHOTS
Exercise 3 - STEPS 1 - 3
Exercise 3 - STEP 4
Exercise 3 - STEP 5
Exercise 3 - STEPS 6
Exercise 3 - STEP 7
Exercise 3 - STEP 8
Exercise 3 - STEP 9
SOME DEFINITIONS:
Integrated Assignment
Final slides
Goal
DATASET 1
Background
Data processing
PART 1: run g:Profiler
PART 2: save as Generic Enrichment Map output (BE)
PART 3: save as Generic Enrichment Map output (NE)
PART 4: create an enrichment map
Answers g:Profiler
PART 5: GSEA (run and create an enrichment map)
Answers GSEA
PART 6: iRegulon
DATASET 2
PART 1: ReactomeFI
Answers REACTOME FI
PART 2: GeneMANIA
Integrated Assignment Bonus - Automation
Goal of the exercise:
Module 08 -1 lab - scRNA pathway analysis using g:Profiler {#scRNA gprofiler-lab}
Introduction
Goal of the exercise 1
Data
Exercise 1 - run g:Profiler
Step 1 - Launch g:Profiler.
Step 2 - input query
Step 3 - Adjust parameters.
Step 4 - Run query
Step 5 - Explore the results.
Step 6: Expand the stats tab
Step 7: Save the results
Step 8 (Optional but recommended)
Exercise 2 - Cytoscape/EnrichmentMap
Goal of the exercise
Data
EnrichmentMap
Description of this exercise
Start the exercise
Step 1
Step 2
Step 3: Explore Detailed results
Step 4 [OPTIONAL]: AutoAnnotate the enrichment map
Exercise 3 [OPTIONAL]- Cytoscape/EnrichmentMap using multiple datasets
Goal of the exercise
Data
Start the exercise
Step 1
Step 2
Published with bookdown
Pathways
Module 1 - Introduction to Pathway and Network Analysis (Gary Bader)
Lecture
Recorded Lecture 1
Recorded Lecture 2