Hello , Thank you for your insightful question

1. Install and load required libraries:

First, you need to install and load the circlize package, which is a powerful tool for circular visualizations, including GOChord plots.

install.packages("circlize")

library(circlize)

2. Prepare the data:

You need data that includes multiple conditions (e.g., different categories or attributes). Your data should have columns representing the conditions. For example, the data might include GO terms or other categorical variables with their associated values for multiple conditions.

Here’s an example of how your data might look:

# Example data frame

data

Hadeer Majed thank you for your detailed answer. However I still did not get how to build my data also with the GO terms. In this case I want my data to show both logFC values in 4 different condition of for example 200 genes, and I want to bound these genes to their corresponding GO term clusters. How can I add this parameter to the matrix?

It was my pleasure to assist you

To incorporate GO terms (Gene Ontology terms) into your data, which contains logFC values across different conditions for multiple genes, you need to merge the GO term information for each gene with your existing expression data. Here's a step-by-step guide on how you can achieve this:

1. Retrieve GO terms for each gene:

You can use public databases like Ensembl, NCBI, or UniProt to get the GO terms for the genes you're studying.

Alternatively, you can use bioinformatics tools and libraries like biomaRt in R or mygene in Python to fetch GO terms based on gene identifiers (e.g., Gene ID or Ensembl ID).

2. Link genes to GO terms:

After retrieving the GO terms for each gene, you can merge this information with your existing gene expression data (which includes logFC values) using the gene identifiers.

For example, in R, you can merge the gene expression table with the GO terms table using the merge() function based on the gene identifiers.

3. Create the final data matrix:

After merging the GO terms with your gene expression data, you will have a data matrix containing logFC values for each gene across the conditions and their associated GO terms.

Your data matrix could have columns like this:Gene ID: Gene identifier

logFC Condition 1: logFC value for condition 1

logFC Condition 2: logFC value for condition 2

logFC Condition 3: logFC value for condition 3

logFC Condition 4: logFC value for condition 4

GO Term(s): GO term(s) associated with each gene

4. Group genes into GO term clusters:

If you want to group genes based on their GO terms into clusters, you can perform GO enrichment analysis or clustering to categorize genes into functional clusters based on shared GO terms.

In R, the package clusterProfiler can help you perform GO enrichment analysis and visualize functional clusters.

5. Add GO term clusters to your data matrix:

Once you have grouped genes into GO term clusters, you can add a new column to your data matrix that represents the GO term cluster each gene belongs to.

6. Example Code in R:

# Load biomaRt library to fetch GO terms

library(biomaRt)

# Connect to Ensembl database

mart