Functional analysis with freeCount | R bloggers

Overview

Functional analysis is useful for determining the functions of differentially expressed genes. Genes can have multiple functional annotations, so we need to determine which ones are important.

What biological functions drive the differences in gene expression?

The free phone The FA app helps you perform functional analyzes of gene sets, which can be produced based on differential expression or network analysis.

Learning objectives

• Learn how to perform downstream functional analysis with topGO
• Practice interpreting functional analysis results
• Understand how to link genes to functions

Related

This tutorial is the second in a series and right after the Creating DE gene lists with freeCount self-study.

topGO

The topGO The R package provides tools for testing gene ontology (GO) terms, taking into account the topology of the GO graph.

One of the key benefits of topGO is the unified gene set testing framework it provides. There are a number of test metrics and algorithms that deal with GO graph structure that are ready for use in topGO.

Gene ontology

The gene ontology (GO) is the logical structure that describes the full complexity of biology. The GO terms describe the many different types of molecular functions (MF), pathways that carry out different biological programs (BP), and cellular locations where they occur (CC).

The GO annotations are traceable, evidence-based statements that relate a specific gene product to a specific ontology term. The set of all GO annotations associated with a gene provides a description of its biological role.

Before you start

The exercise in this tutorial uses the freeCount apps in RStudio on a PC. Make sure you have downloaded, installed, and installed the following tools up to date on your PC:

1. R software environment
2. RStudio desktop application

For Windows usersadditionally install RTools.

It is also possible to run the freeCount apps online via Posit Cloud. To see how, check out the freeCount Bioinformatics analysis apps on Posit Cloud self-study.

Input data

1. Download the tribolium DE gene lists file
2. Download the tribolium GO term annotations file

Tip! Right click and select Save as… to download the above files in the necessary formats.

The Analysis App

The following steps show you how to download and use the freeCount functional analysis (FA) app.

1. Download the free Count R Shiny applications
   1. Go to https://github.com/ElizabethBrooks/freeCount
   2. Click on the green < > Code knob
   3. Click Download ZIP
2. Grab the freeCount-main folder
3. Navigate to the apps folder
4. Open the FAR file in RStudio
5. Click Install on the yellow banner to install the necessary R packages (or run the code on lines 10 through 20)
6. Click on the Run app button in the top right corner of the source panel

Analysis process

Complete the following steps to create a list of key GO terms, which describe the biological functions driving the differences in gene expression in your experiment.

1. Upload the data and click Perform analysis
2. Check the initial settings on the Analysis tab
3. Explore the data and initial results for each ontology level
4. Adjust the P-value, Algorithm, or Test Statistic settings and click Update analysis
5. Create a curated list of GO terms by repeating steps 3 and 4
6. Download the compiled list of GO conditions

1. Upload data

Upload the data and click Perform analysis.

Input data

1. The first text box is the Gene score statisticwhich is the metric used to filter your gene sets to focus on interesting genes (e.g. significantly DE). In this tutorial we need to set this to FDR.
2. The second text box is the Expression for gene scores which defines how to filter your gene sets using the specified metric in the first text box. In this tutorial we need to set this to <0.05.
3. It’s the first file Gene score table in which all genes have been detected in your experiment with gene-wise scores. In this tutorial we will use one tribolium DE gene lists file.
4. The second file is the Allocations table with the GO term annotations for your experiment. In this tutorial we will use the tribolium GO term annotations file.

2. Check the initial settings

Check the initial settings on the Analysis tab.

3. Explore data

Discover the data and first results for each ontology level (BP, MF or CC) on the Exploration And Results tabs.

View the number of significant GO terms for each ontology level on the Exploration tab. The histogram shows the range of p-values, allowing you to see how many GO terms turned out to be significant for your list of genes at the current analysis settings.

The table of Results for the main significant GO terms on the Exploration The tab shows the most important GO terms for the selected ontology level, sorted by p-value.

Results of the GO term functional analysis can be viewed at the Results tab. The scatter plot shows the most significant GO terms for each ontology level.

4. Adjust settings

Adjust the P-value, Algorithm, or Test Statistic settings and click Update analysis.

Filter functional analysis results

Adjust the cutoffs Through…

• Lowering the P-value to focus on High probability GO terms
• Changing the Algorithm or Test statistic to make different assumptions and tests on the data

Limit the results to the GO terms related to biological functions that you believe are driving the differences in gene expression and that appear to be relevant to your experiment.

Check the P-value threshold, algorithm, and test statistic by examining the resulting GO terms for the different ontology levels. One approach is to search online databases (e.g. FastGO) for more information about the important GO conditions. It can also be useful to use AI tools to explore the results of the GO term.

Check analysis settings

Verify that the analysis settings have been updated by looking at the file Current analysis settings on the left side of the app.

5. Create a curated list

Create a curated list of GO terms by repeating steps 3 and 4.

You may need to repeatedly adjust the settings and inspect the functional analysis results to create a well-organized list of GO terms relevant to your experiment.

Additionally, you may want to go back and adjust the DE analysis settings to create a more informative set of genes for the functional analysis.

6. Download results

Download the compiled list of GO conditions.

The Table of GO term results has sorted all GO terms in the analysis (significant or not) by p-value.

The Table of significant GO term results sorted all significant GO terms in the analysis by p-value. These GO terms can be searched using the web or AI tools to identify the relevant terms for your experiment. Keep in mind that it’s important to double-check results from AI tools because they can report incorrect functions for terms.

The Table of gene IDs for GO terms has only the gene IDs for each of the GO terms, which can be used in set operations to identify shared or unique sets of genes. This table can be entered into the freeCount SO app to perform set operations.

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