Cancer Analysis
I Introduction
Workshop Info
Pre-work
Class Photo
Toronto:
Montreal:
Schedule
Meet Your Faculty
TA’s
Data and Compute Setup
II Modules
Keynote by Jessica Rosenbloom
Presentation
Module 1 Intro to Cancer Genomics
Lecture
Module 2 Understanding and Visualizing Data
Lecture
Lab
Part 1 - IGV
Part 2 - cBioPortal
Part 3 - COSMIC
Part 4 - Advanced Exercises
Module 3 Genome Alignment
Lecture
Lab
Module 4 Somatic Alterations
Lecture
Lab
Introduction to Variant Discovery and Annotations in Cancer Data
Getting started
Variant Discovery in Cancer
Functional Annotations
Beyond VEP: what else does PCGR add to this INFO field
Exit the container environment
Module 5 Copy Number Alterations
Lecture
Lab
Dataset
Run cobalt , amber , purple
Run Genpipe
Analyze the purple results
Some examples of Gain, Loss, Deletion and LOH
Visualize a gene or a chromosome
Exercises
Module 6 Transcriptomics RNA-seq
Lecture
Lab
0. Introduction to the dataset
1. Setting the environment variables
2. Explore data in your workspace
3. Trimming and QC
4. Prepare the reference genome for alignment
5. Alignment with HISAT2
6. Gene count estimation
7. Differential Expression Analysis
Module 7 | From Gene Alteration to Gene Effect
Lecture
Lab
Part 1. Introduction
Part 2: Inspect the data
Part 3. Summarize the variant calls with bcftools
Part 4. Annotate variants with SnpEff
Part 5. Interpret the SnpEff HTML summary
Part 6. Examine individual variant annotations
Part 7. From gene function to cancer interpretation
Part 8. Compare SnpEff and Ensembl VEP
Part 9. Final interpretation
Part 10. Manually confirming variants in IGV
Module 8 - Pathway Enrichment Analysis
Lecture
Presentation of the dataset
Lab
Module 8.1 Lab: Differential Expression
Install and load libraries
Data used for this comparison
generate a DGE object
Filter low-count genes and TMM-normalize
Design (~0 + group) and named contrast
Dispersion estimation and quasi-likelihood GLM
Calculating ranking score and export full results
Result output
Version of packages and R
Module 8.2 Lab: Defined gene list (g:Profiler)
Introduction
Goal of the exercise 1
Data
Exercise 1 - prepare the gene lists
Step 1 - Install and Load libraries
Step 2 - Prepare the define gene lists
Step 3 - Run g:Profiler
Clear visualization of the results
Step 4 - Explore top table of results
Step 5 - Filter results by geneset size
Step 6 - Run g:profiler with your own genesets (example using BaderLab genesets)
Exercise 2 - visualization
Step 2 - Dot plot with segment
Step 4 - Hierarchical Cluster
Module 8.3 Lab: Ranked gene list (fGSEA)
Introduction
Goal of the exercise
Data
Background
Exercise 1 - prepare the gene lists
Step 1 - Install and Load libraries
Step 2 - Prepare the rank file , upload the gmt file and run fgsea
Step 3 - Examine the results
Step 4 - Explore the tabular format of the results
Step 5 - Visualize the top results
Module 8.4 Lab: BYOD - Differential Expression (Palbo-resistant vs Abema-resistant)
Install and load libraries
Data used for this comparison
generate a DGE object
Filter low-count genes and TMM-normalize
Design (~0 + group) and named contrast
Dispersion estimation and quasi-likelihood GLM
Calculating ranking score and export full results
Result output
YOUR TURN
Additional exercise
Module 9 - Variant to Networks
Network Visualization and Analysis with Cytoscape
Lecture
Lab
Module 9 Lab Primer: Cytoscape Primer
Goal of the exercise
Data
Start the exercise
Exercise 1a - Create Network from table
Setting up cytoscape variables -
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 9.2 Lab: g:profiler Visualization
Goal of the exercise
Data
EnrichmentMap
Description of this exercise
Start the exercise
Exercise 1a - compare UP and DOWN regulated g:Profiler results
Step 1
Step 2
Step 3: Explore the results:
Explore Detailed results
Exercise 1b - create EM from fgsea results using Baderlab genesets
Exercise 1c (optional) - investigate individual pathways in GeneMANIA or String
GeneMANIA
String
Module 10
Lecture
Lab
Sponsors
Cancer Analysis 2026
Keynote by Jessica Rosenbloom
Presentation