Description Target Audience Prerequisites Outline

Course Description

The CBW has developed a 2.5 day course covering the bioinformatics concepts and tools available for interpreting a gene list using pathway and network information. The workshop focuses on the principles and concepts required for analyzing and conducting pathway and network analysis on a gene list from any organism, although focus will be on human and model eukaryotic organisms.

Please note that this listing is for the Asynchronous version of this workshop. We are also offering an in-person version in June; you can see details for that offering here.

Which Pathway and Network Analysis course should I take?

We are offering Pathway and Network Analysis in two formats in 2024. Both formats will cover the same material, and both will have instructors and TAs available to provide help and answer questions.

In-person is our classic 2.5-day intensive workshop held in Toronto. This workshop is best for those who like to immerse themselves in the material and receive 1:1 attention from instructors and TAs throughout.

The asynchronous virtual format happens over five weeks: each Monday, lecture(s) and lab material are released for participants to work through during the week, and short assignments are due at the end of the week to assess progress. While participants complete most tasks autonomously, they can ask questions and receive instructor/TA help on the course discussion boards or at the instructors’ live/recorded virtual office hours (1 hour per week, Thursdays). This format is great for self-paced learners who like to revisit material as they go and those who prefer online learning.

Course Objectives

Participants will gain practical experience and skills to be able to:

Get more information about a gene list;
Discover what pathways are enriched in a gene list (and use it for hypothesis generation);
Find out how a set of genes is connected by e.g. protein interactions and identify pathways, systems and modules within this network;
Predict gene function and extend a gene list;

We will develop a unified analysis flow chart throughout the course that students will be able to follow after the workshop to conduct their own analysis.

Target Audience

This workshop is intended for biologists working with ‘Omics data’ (e.g. gene expression, protein expression, molecular interactions, large-scale genetic screens and other omics data) from human and model eukaryotic organisms who are interested in interpreting large gene lists resulting from their experiments. Concepts will be applicable to omics data from non-eukaryotic organisms, but software and demonstrations will not cover them. The workshop does not cover preprocessing of the ‘Omics data’ (e.g. normalization, differential expression analysis).
Tools presented in this workshop do not require any programming skills and the workshop does not involve any coding. However, for R proficient users, scripts will be provided for some of the pipelines (pathway enrichment analysis, EnrichmentMap).

Prerequisites

You will also require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 8GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).

This workshop requires participants to complete pre-workshop tasks and readings.

Course Outline

Module 1: Introduction to pathway and network analysis

Where gene lists come from and what they are useful for
Pathway and network analysis overview
Workflow of concepts and tools from gene list to pathway analysis
Examples of multiple paths through the workflow that will be covered in the workshop
Sources of pathway and network information: GO biological process, network databases, pathway databases. Examples of pros and cons of each type of information.
General issues: gene identifiers, data normalization

Module 2: Finding over-represented pathways in gene lists

Statistics for detecting over-representation e.g. hypergeometric test, GSEA
Multiple testing correction: Bonferroni, Benjamini-Hochberg FDR
Filtering Gene Ontology e.g. using evidence codes

Lab Practical: Performing over-representation analysis (covers many model organisms):

Workflow of tools and steps
g:Profiler tool for over-representation analysis
Gene Set Enrichment Analysis (GSEA) and Enrichment Maps software tool
Running gene enrichment tools on your gene list

Module 3: Network visualization and analysis with Cytoscape

Introduction to Cytoscape and basic network concepts
Cytoscape demo
EnrichmentMap

Lab Practical: Tutorials on Cytoscape:

EnrichmentMap
AutoAnnotate
Layouts
Labels

Module 4: More Depth on Pathway and Network Analysis

Basic network concepts
Types of pathway and network information
Network and pathway databases
More examples of pathway and network analysis methods
Reactome analysis tools: network clustering and paradigm

Lab Practical: Cytoscape ReactomeFIPlugIn (for human data only):

Workflow of tools and steps (ReactomeFI intro +demo)
ReactomeFIPlugIn lab

Module 5: Gene Function Prediction

Functional association networks and gene function prediction
Functional relationships, similarity space
Guilt-by-association concept
GeneMANIA and STRING tools

Lab Practical:

Using GeneMANIA to assess gene and gene list function
Workflow of tools and steps (GeneMANIA intro + demo) (30min)
GeneMANIA lab
stringApplab

Module 6: Cell-Cell Communications

Introduction to single-cell RNA seq
Cell cell communication
Presentation 1 or 2 tools for studying cell-cell communication in scRNA (cellphoneDB as example)
Introduction to spatial transcriptomics
Presentation of NEST (NEural network on Spatial Transcriptomics) for cell cell communication and spatial transcriptomics

Lab Practical: exploring tools for scRNA and/or cell-cell communication

Module 7: Review of the tools in an integrated workflow pt.1

Familiarize yourself with g:Profiler, GSEA , EnrichmentMap in a workflow.
Review of top 10 Cytoscape apps and final words. Question and Answers.

Option 1. Work with your own data. Use the tools reviewed in this workshop with your own gene list. Questions and Answers with instructors
Option 2. Example of pathway analysis using scRNA
Option 3. Review Cytoscape basic tools like playing with styles, creating subnetworks. Learn how to use the iRegulon to find predicted targets of transcription factors in your gene list.
Option 4. Example of pathway analysis using ChIP-seq data

Module 7: Review of the tools in an integrated workflow pt. 2

Option 1. Work on your own data and project. Questions and answers with instructors.
Option 2. For R users only (optional) – integrate the enrichment map pipeline directly into your current R pipelines. Learn how to communicate with Cytoscape directly from R using the bioconductor package RCy3. Create, manipulate and modify networks automatically.
Option 3. For R users only (optional). Explore the clusterProfiler R package. A universal enrichment tool for interpreting omics data.
Option 4. Finish practical labs from the workshop at your own pace. Questions and answers with instructors.