CBW Pathways Workshop
Canadian Bioinformatics Workshops
Welcome
Meet your Faculty
Gary Bader
Lincoln Stein
Gregory Schwartz
Veronique Voisin
Ruth Isserlin
Chaitra Sarathy, PhD
Nia Hughes
Class Materials
Workshop Schedule
Pre-Workshop Materials and Laptop Setup Instructions
Laptop Setup Instructions
Basic programs
Cytoscape Installation
GSEA Installation
Docker Installation
Pre-workshop Tutorials
Cytoscape Preparation tutorials
R Tutorial
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)
Step 9 (Optional by recommended)
Exercise 2: Load and use a custom .gmt file and run the query
Optional steps
Optional 1
:
Option 2
:
Option 3
:
Bonus - Automation.
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
Bonus - Automation.
Module 3: Network Visualization and Analysis with Cytoscape
Module 3 Lab Primer: Cytoscape Primer
Goal of the exercise
Data
Start the exercise
Exercise 1a - Create Network from table
Exercise 1b - Load node attributes
Exercise 1c - Map node attributes to Visual Style
Exercise 2 - Work with larger networks
Exercise 3 - Perform basic enrichment analysis using EnrichmentTable
Exercise 3B - create Enrichment Map and Enhanced graphics nodes from EnrichmentTable
Exercise 4 - Load network from NDex
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 1d (optional) - investigate individual pathways in GeneMANIA or String
GeneMANIA
String
Bonus - Automation.
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
Bonus - Automation.
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
Running example notebooks in local RStudio
Step 1 - launch RStudio
Step 2 - create a new project
Step 3 - Open example RNotebook
Step 4 - Step through notebook to run the analysis
Exercises
Additional resources
Module 4: In-depth Analysis of Networks and Pathways
Module 4 Lab: ReactomeFI
Goal of this practical lab
Data: download the following files on your computer before starting the practical lab.
Exercise 1: Use the Reactome Functional Interaction (FI) Network
Question 1: Describe the size and composition of the network?
Question 2: After clustering, how many modules are there?
Query information about the interaction between 2 genes:
Question 3: What are the most significant pathways in each module?
Set the size of the nodes proportional to the mutation frequencies in each cancer
Play around with the styles: change transparency and colors
Create a pie chart
Create a subnetwork
Fetch Cancer drugs on the created subnetwork
Save the network as an image for publication
Exercise 2a: Explore Reactome Pathways
Exercise 2b: Pathway enrichment analysis using a simple gene list
Question 1: What are the most significant biological pathways based on the FDR?
Answer to Question 1
Exercise 2c: Pathway-based analysis using a rank gene list (GSEA)
Automation ( for advanced users)
Reference guide /bonus exercises:
Module 5: Gene Function Prediction
Module 5 Lab: GeneMANIA (Cytoscape version)
Goal of this practical lab
EXERCISE 1: Searching GeneMANIA with single gene
ANSWERS
EXERCISE 2: Searching GeneMANIA with gene list
EXERCISE 3: Searching GeneMANIA with mixed gene list
GeneMANIA DEFINITIONS:
EXERCISE 4 (OPTIONAL): Discover the stringApp
More STRING information and tutorials:
Module 5 Lab: GeneMANIA (web version)
Goal of this practical lab
EXERCISE 1: QUESTIONS AND STEPS TO FOLLOW
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
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
Exercise 3: MORE DETAILS AND SCREENSHOTS
Exercise 3 - STEPS 1 - 3
Exercise 3 - STEP 4/ STEP5
Exercise 3 - STEPS 6
Exercise 3 - STEP 7
Exercise 3 - STEP 8
Exercise 3 - STEP 9
SOME DEFINITIONS:
Module 6: Cell Cell Communication
Module 6 lecture : Cell-Cell Communication.
scRNA lab praticals
Module 6 lab 1: scRNA PBMC
Introduction
Pmbc3k Seurat Pipeline
load libraries
Load the PBMC dataset
Process the dataset
Assign cell type identity to clusters
Find differentially expressed features (cluster biomarkers)
Create Gene list for each cluster to use with g:Profiler
Data (gene lists for each cluster)
Run pathway enrichment analysis using g:Profiler
Create an enrichment map in Cytoscape
GSEA from pseudobulk
pseudobulk creation, differential expression and rank file
run GSEA:
Create an EnrichmentMap:
Module 6 lab 2- scRNA Glioblastoma
Introduction
Goal
Data
Overview
Part 1 - run g:Profiler [OPTIONAL]
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)
Part 2 - Cytoscape/EnrichmentMap [OPTIONAL]
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
Part 3 - Master map using multiple datasets
Goal
Data
Start the exercise
Step 1
Step 2
Module 6 lab 3: cellPhoneDB
Cell-Cell communication in scRNA: CellPhoneDB
Presentation
Method
Examining the results
Visualization using Cytoscape
Dataset and references
Dataset preprocessing and running CellPhoneDB
Module 6 lab 4: NEST
Cell-Cell Communication (CCC) in spatial transcriptomics using NEST
NEST (NEural network on Spatial Transcriptomics)
How to run NEST
Practical lab : Pancreatic Ductal Adenocarcinoma (PDAC)
Module 7: Review of the tools
Final slides
Integrated assignment
Integrated assignment bonus
ClusterProfiler
Module 7 Integrated Assignment
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)
PART 6: iRegulon
DATASET 2
PART 1: ReactomeFI
Answers REACTOME FI
PART 2: GeneMANIA
Answers GeneMANIA
Module 7 Integrated Assignment Bonus - Automation
Goal of the exercise:
Optional Module 8: 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
Optional Module 8 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
Optional Module 8 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.
Use our precomputed iRegulon results:
Notes about iRegulon:
Notes about Cytoscape:
Exercise 3. Use Enrichr with the prostate gene list.
Goal
Steps
end of practical lab
Optional Module 8 Lab 3: Automated Enrichment and Visualisation Lab using
clusterProfiler
clusterProfiler
lab
Goal
Supported Analysis
Install and load packages
Exercise 1a. Over representation analysis
Data for enrichment using
clusterProfiler
Data for over representation analysis using
clusterProfiler
Perform GO over representation analysis
Results of GO over representation analysis
Input options for
enrichGO()
:
Simplify
enrichGO()
results
Exercise 1b. Visualise the results of GO over representation analysis
Barplot
Dotplot
Enrichment Map
Upset plot
Details about the input arguments for
enrichGO()
A note on supported organisms
Exercise 2a: Gene set enrichment analysis
Data for running gene set enrichment analysis in
clusterProfiler
Perform GO gene set enrichment analysis
Results of GO gene set enrichment analysis
Input options for
gseGO()
Details about the input arguments for
gseGO()
Exercise 2b. Visualise the results of gene set enrichment analysis
Dotplot
Ridgeline plot
Running score and preranked list of GSEA result
Enrichment Map
What next?
Explore other features of
clusterProfiler
Bonus - Try it yourself:
Ontologies and pathway databases supported by
clusterProfiler
All publications describing
clusterProfiler
can be found here:
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Pathway and Network Analysis of -Omics Data ( June 2024 )
Module 1 - Introduction to Pathway and Network Analysis (Gary Bader)
Lecture
Recorded Lecture 1