Enrichment Map GSEA Tutorial

Enrichment Map GSEA Tutorial v1 | Charu Gupta Kumar

1

Enrichment Map GSEA Tutorial

Charu Gupta Kumar

Powerpoint by Casey Hanson


2

Exercise

In this exercise we will be doing the following:

1. Conducting GSEA on a set of genes probed in Estrogen Treated (ES) human cells and Not Estrogen Treated (NT) cells at the 12 hr, 24 hr, and 48 hr mark.

2. Visualizing the result of running GSEA on our dataset against GO Pathway gene sets using the EnrichmentMap plugin for Cytoscape.


3

Step 0: Shared Desktop Directory

For viewing and manipulating files on the classroom computers, we provide a shared directory in the following folder on the desktop:

classes/mayo

In today’s lab, we will be using the following folder in the shared directory:

classes/mayo/gupta


4

Gene Set Enrichment AnalysisIn this exercise, we will perform a Gene Set Enrichment Analysis (GSEA) on gene expression (microarray) data in humans. The phenotypes of interest are cell lines that were treated with estrogen (ES) and not treated with estrogen (NT) at certain time points.We will use GSEA, to find out which GO Pathways our dataset is significantly enriched in. We will conduct the analysis at the 12 hr and 24 hr marks


5

Dataset Characteristics I

Name Meaning

ES_NT.clsThe list of phenotypes for the cell lines (ES or NT) at different time points: 12, 24, and 48 hours.

MCF_ExpMX_v2.names.gct

Expression of genes for each of the phenotypic conditions. A total of 18 such conditions.

Human_GO_AllPathways_no_Go_iea_April_15_2013_symbol.gmit

GO Pathway gene sets in human.


6

Dataset Characteristics II

An excerpt of the gene expression data given MCF_ExpMX_v2.names.gct is shown below:

genes

Treated With Estrogen @ 12 Hours


7

Step 1A: Starting the GSEA ToolIn this step, we will start GSEA and load our necessary data.

Double click the gsea2-2.0.jar icon on the Desktop.

Click the Load data button.

On the next page, select Browse for files.


8

Step 1B: Starting the GSEA ToolIn the file browser, navigate to our shared directory at classes/mayo/gupta/.

Select the .cls, .gct, and .gmt files and hit Open.

Wait for the confirmation window to pop-open indicating it loaded and hit OK.


9

Step 2A : Configure GSEA to Run

In this step, we will start our GSEA runs.

Select Run GSEA from the Steps Pane

On the resulting page, select MCF7_ExprMx_V2_names for our expression dataset.


10

Step 2B: Configure GSEA to RunFor Gene sets database, click the button adjacent to the textbox.

On the pop-up window select the Gene Matrix (local gmx/gmt) tab.

Select the GO .gmt file and click OK.


11

For Phenotype labels, click the button adjacent to the textbox.

On the pop-up window select the ES_NT.cls as the source file.

In the window, select ES12_versus_NT12 and click OK.

We will run our analysis on the 12 hr time point first between Estrogen Treated (ES) and Not Treated (NT) conditions.

Step 2C: Configure GSEA to Run


12

Step 2D: Configure GSEA to Run

For Collapse dataset to gene symbols select false.

For Permutation type select gene set.

Click Show adjacent to the Basic fields header.


13

Step 2E: Configure GSEA to Run

For the analysis name, type the following:

estrogen_treatment_12hr_gsea_enrichment_results


14

Step 2F: Configure GSEA to Run

Adjacent to the Save Results in this folder textbox, click:

In the popup window, navigate to the Desktop and create a folder by clicking:

Name the folder the following:

estrogen_treatment_12hr_gsea

Click the Select button.


15

Step 3: Running GSEA

At the bottom of the GSEA window click the Run button.

Unfortunately this will take a while …

Finished runs for this exercise are located in the following directory:

mayo/gupta/results/


16

Step 3B: Re-running GSEA

Perform the same analysis comparing the 24 hr time points.

To do this, change our Phenotype Labels to ES24-NT24

Change the Analysis Name under Basic Fields to the following:

estrogen_treatment_12hr_gsea_enrichment_results

Select a new output folder on the desktop called the following:


Click Run


17

Cytoscape GSEA VisualizationIn this exercise, we will visualize and interpret the results of our GSEA analysis between ES and NT at 12 hr and 24 hr in Cytoscape.


18

Step 4: Open Cytoscape 2.8.2

Double click the Cytoscape 2.8.2 icon


19

Step 5A: Install Enrichment Map Plugin for Cyotoscape 2.8.2 Open the following link in a web browser:

http://www.baderlab.org/Software/EnrichmentMap?action=AttachFile&do=view&target=EnrichmentMapPlugin_v1.2.zip

Download EnrichmentMapPlugin_v1.2.zip to the Desktop.

Extract the contents of the zip file to the Desktop.

You should have README.txt, LICENSE.txt, and EnrichmentMap.jar files on the Desktop now.





20

Step 5A: Install Enrichment Map Plugin for Cyotoscape 2.8.2 On the Menu Bar, click Plugins.

Then click Install Plugin from File.

In the pop window, navigate to the EnrichmentMapPlugin_v1.2 directory on the Desktop.

Select EnrichmentMap.jar

Click open.


21

Step 5A: Configure the GSEA Plugin

To load the GSEA Plugin for Cytoscape, click Plugin from the Menu Bar.

Under Plugin, select Enrichment Map and Load Enrichment Results.


22

Step 5B: Configuring the GSEA PluginUnder Analysis, select GSEA.

We will load our results of GSEA on the 12hr data.

Click the button adjacent to the Expression textbox underneath the Dataset 1 header.


23

Step 5C: Configuring the GSEA Plugin

In the pop up box, navigate to the your output directory for the 12 hr. run:


Identify the RPT file (shown)

Click Open


24

Step 5C: Configuring the GSEA PluginSet the P-value cutoff to 0.01

Set the Q-value cutoff to 0.05

Set the Overlap Coefficient Cutoff to 0.05.

Click Build.


25

Step 6: Analyzing GSEA Visualization The resulting browser view should look similar to the bottom screenshot.


26

Step 7: Cytoscape Legend

Nodes indicate GO Pathway Gene Sets.

The color of the Node indicates what phenotype that GO term is most enriched in (Green/Red = ES, Blue = NT).

The size of the nodes indicate how many genes are contained within the nodes.

Edges associate those GO terms with genes in common.


27

Step 8A: Results

GO terms enriched in ES share many genes in common.

The same applies to those GO terms enriched in NT.

Obvious, but important result. Indicates the GSEA is working appropriately.


28

Step 8B: Results

Looking at the bottom cluster, there appears to be a cluster of related GO terms significantly enriched in the ES dataset and that share many common genes.

Documents

Enrichment Map GSEA Tutorial