Pathway and Network Analysis
I Introduction
Workshop Info
Pre-work
Class Photo
Schedule
Meet Your Faculty
II Modules
Module 1
Lecture
Module 2
Lecture
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)
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.
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
Lecture
Lab: 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
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.
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
Lecture
Lab
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
Lecture
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:
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
Lecture
scRNA lab praticals
Lab 1: PMBC
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:
Lab 2: Glioblastoma
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
Lab 3: cellPhoneDB
Presentation
Method
Examining the results
Visualization using Cytoscape
Dataset and references
Dataset preprocessing and running CellPhoneDB
Lab 4: NEST
NEST (NEural network on Spatial Transcriptomics)
How to run NEST
Practical lab : Pancreatic Ductal Adenocarcinoma (PDAC)
Module 7
Lecture slides
Lab: 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
Integrated Assignment Bonus - Automation
Module 8
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
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
Sponsors
Pathway and Network Analysis 2024
Module 1
Lecture