After RNA-seq data analysis, you will get a list of differentially expressed (DE) genes. In this exercise, you will use two different ways to test which function categories are enriched in your DE gene list.
There are three GO domains: cellular component (CC), biological process (BP), and molecular function (MF). During the exercise, you will only do function enrichment test for the BP domain. When you work on your real project, you will need to test all three.
1.1 Make a working directory and copy over data files for this exercise
1.2 Install GSEA on your laptop.
Go to http://software.broadinstitute.org/gsea/index.jsp and click "Downloads" to download and install the software.
(Optional) Install Cytoscape. Go to https://cytoscape.org/ and click "Download x.x.x" to download and install the software. After installation, you will need to install an app within Cytoscape named "EnrichmentMap". Start Cytoscape. Click "App"->"App Manager". Search for "EnrichmentMap" and install this app.
The topGO package of Bioconductor will be used to do ORA. You will perform ORA on RNA-seq results of a yeast experiment.
2.1 Download GO annotation from Ensembl BioMart.
Ensembl (https://ensembl.org/) is a good resource to retrieve existing GO annotation (For plant genomes, go to https://plants.ensembl.org/ )
2.2 Convert the GO annotation file into a format compatible with topGO. Use FileZilla to upload the "mart_export.txt" file to your assigned Linux server, keep it in the directory "/workdir/$USER/exercise3".
We provide you with a script that converts the BioMart file into topGO format. Run the commands:
After this, you would see a new file created: topgoAnnot.
Check the difference between the original file "mart_export.txt" and the converted file "topgoAnnot".
If a gene has multiple GO accessions, in the file "mart_export.txt", each of these GO accessions are in separate lines. In the the converted file "topgoAnnot", all GO accessions of the same gene are in a single line.
2.3 Run topGO
You are provided with two gene lists: refset and DElist_pos.
Check the content of these two files:
Run the R script topGO.r:
topgoAnnot, refset and DElist_pos: three input files;
0.1: cutoff p-value for reporting enriched GO categories.
BP: test for biological processing GO. You can also test for MF (molecular function) and CC (cellular component).
myBP: output file name prefix.
After this, you should find two new files "myBP".
You can use FileZilla to download both files to your laptop to examine the contents.
The myBP file can be opened with a text editor. You can copy-paste each section in this file into an Excel spreadsheet (using text import wizard with "fixed width"). The enriched GO ids are sorted by p-values in the column "topgoFisher".
When you write "method" section of your manuscript, you can write that "gene set enrichment analysis was performed with BioConductor topGO package, using its default weight01 algorithm and topgo Fisher test, and p-value threshold is set at ...".
We provide you with two data files (bp.gmt & genes.rnk) to run GSEA. When you work on your own project, here is how to prepare the two files.
You can run GSEA either on your laptop, or on the LINUX server.
3.1 Run GSEA on laptop.
Use FileZilla to download the two data files bp.gmt & genes.rnk from the directory /shared_data/RNAseq-function/.
Click "Load data", then click "Browse for files", and open the two files bp.gmt & genes.rnk
Click "Run GSEA Preranked", and set the following:
Click "Run". It would take a few minutes. After it is done, you should see "Success".
*Enrichment statistics: the high p value would put more weight to genes with high fold change when calculating enrichment scores (ES). Use the default "Weight" in most cases.
3.2 Examine results Click "Success". If it does not work, click "Show results folder", which should open the directory. Open the directory of your analysis, double click the file "index.html".
On the page, you should see two block of "Enrichment in phenotype". One for gene sets enriched in up-regulated genes (na_pos) and one for gene sets enriched in down-regulated genes (na_neg). Click "Detailed enrichment results in html ", you would see enriched gene sets.
3.3 (Optional) Enrichment map visualization.
Interpretation of the map can be found at this page: https://enrichmentmap.readthedocs.io/en/docs-2.2/Tutorial_GSEAInterface.html
3.4 (Optional) Run GSEA command on LINUX
With GSEA open on your laptop, click "command" at the bottom of the GSEA window. Copy-paste the command to a text editor.
You need to change a few things for this command to run on BioHPC computer:
Now run the command on Linux server. After done, you can examine the results in the "out" directory.