• There are several methods of functional enrichment that can be used from various web tools and programming languages.
• We will start using the web tool PANTHER where we will perform several approaches to know the functions in which our list of genes of interest are participating.

To functionally characterize by means of different enrichment strategies (overrepresentation and GSEA methods), the results obtained in the differential expression analysis of the experiment where we used RNA-Seq in mice, and in which we were interested in detecting differentially expressed genes between these 2 groups: wild type (WT) and treated with T3 hormone.

After performing differential expression analysis between two experimental groups, the following results were obtained:

• Top genes (significant and with higher expression in T3 when we compared T3 vs. WT): 128 genes, top list
• Bottom genes (significant and with higher expression in WT when we compare T3 vs. WT): 419 genes, bottom list
• List of all genes involved in the study, ordered by contrast statistic: list of all ranked genes (16271)}

Open the “top list” data file with a notepad or similar and inspect its contents. Inspect the other two files as well.

• Step 1. From the home of the tool go to the “Gene List Analysis” tab. Upload this txt file “top list” or copy the ids of the genes in the window indicated for this purpose.
• Step 2. Select the organism. In this case “mus musculus”.
• Step 3. Choose the type of analysis. Then we check all the available options:

• We will start with a description of the functions annotated (GO terms, signaling pathways) to these genes and for this we will use these two options: “Functional classification viewed in gene list” and “Functional classification viewed in graphics charts” that will provide us respectively the list and the graphical summary of functions associated to this group of genes.

• We need:
  1. After obtaining the functional classification list of these genes, we will save it in a file (“Send list to file”).
  2. From the option “Functional classification viewed in graphic charts” get a bar chart for each Gene Ontology ontology. The same information, please represent it from a “pie chart”.

• This method provides us with the functions that are overrepresented in our gene list versus the rest of the reference genome.
• The functional results will characterize the genes included in our list of interest.
• We would like to:
  1. Perform an analysis using the PANTHER GO-Slim Biological Processes. The results we obtain will be visualized in a “multiple pie chart”.
     • Interpret each of the indicators that appear in the output.
     • Are there functional differences between the reference genome and our gene cluster?
  2. Repeat the analysis but this time we will use all the Biological Process. Comment on the results.

Then reproduce the previous two points with the “Bottom list” file.

• This functional enrichment method incorporates information of interest (clinical, biological or statistical) that ranks the genes in the list.
• The functional results will characterize all the genes in our experiment, including additional information that weights the genes in this list, in this case according to their differential expression level (the contrast statistic). So input we will need will be: “list of all ranked genes”.
• We would like to:
  1. Perform an analysis using the PANTHER GO-Slim Biological Process.
     • Interpret each of the indicators that appear in the output.
     • What does it mean that there are functions with a positive enrichment indicator and that other functions have a negative enrichment?
  2. Repeat the analysis but this time we will use all the Biological Process. Comment on the results.
  3. Some questions on the comparison of methods: GSA vs. ORA
     1. What is the difference in input between the two methods?
     2. Do you think you will find more results with GSA than with ORA methods?
     3. Will the Gene Sets that participate in the same function and that we detect as significant include only genes that are differentially significantly expressed or may there be genes that are not significant but have a common expression pattern in that Gene Set?

• ClusterProfiler package implements methods to analyze and visualize functional profiles GO, KEGG, DisGeNET, Reactome…) of gene and gene clusters.
• We are going to work the same previous exercises, but from R using clusterProfiler package. Are you ready? This script + data could help us. Questions for you
• More detailed information: Annotation and visualization, clusterProfiler-book

We continue with the example “Functional evaluation of the effect of T3 hormone in transcriptomic study with RNA-Seq data”. In this case we will perform a functional characterization with a GSEA method using R.

In this zipped folder: you will find:

• R script to perform a Gene Set Enrichment Analysis from the mdgsa package.
• Inputs of the analysis:
  • list of all genes involved in a transcriptomic study sorted by their differential expression level.
  • Functional annotation file (gene-function) that we downloaded from BioMart from Ensembl files.

Run the R script:

• Check how the annotation downloaded from BioMart has been refined.
• Determine which functions are over-represented in each of the two groups compared.