How to link DESeq2 results to GO enrichment?

Preparation Before Analysis

• Ensure you have the latest version of R and Bioconductor installed, as these are essential for DESeq2 and GO analysis.

 

• Install or update necessary packages: DESeq2 for differential expression analysis and clusterProfiler for GO enrichment.

Running DESeq2 for Differential Expression

• Prepare a count matrix and a metadata (colData) table that includes sample information relevant to your experimental design.

 

• Create a DESeqDataSet object using the DESeqDataSetFromMatrix function with your count matrix and metadata.

 

• Run the DESeq2 pipeline with the DESeq function on your DESeqDataSet object to perform differential expression analysis.

 

• Extract results using the results function to obtain a table of differentially expressed genes.

Filtering and Preparing Gene List

• Filter your DESeq2 results to obtain a list of significantly differentially expressed genes. Typically, you would filter by adjusted p-value and log-fold change.

 

• Extract gene identifiers from the filtered results for further analysis in the GO enrichment step.

GO Enrichment Analysis

• Load the clusterProfiler package, which will be used for GO term over-representation analysis.

 

• Choose the appropriate annotation database (e.g., org.Hs.eg.db for human) for mapping your genes to GO terms.

 

• Use the enrichGO function from clusterProfiler to perform GO enrichment analysis. Set parameters like the ontology type (BP, MF, or CC), p-value cutoff, and adjust method.

 

• Pass your list of gene identifiers from the DESeq2 results into the gene argument of the enrichGO function.

Interpreting and Visualizing Results

• Inspect the GO enrichment results by examining the output from the enrichGO function, which provides enriched GO terms and associated statistics.

 

• Visualize the enrichment results using visualization functions in clusterProfiler like dotplot, barplot, or emapplot to create intuitive plots of GO terms.

Finalizing the Workflow

• Interpret the biological meaning of the enriched GO terms in the context of your study to generate hypotheses or guide further experiments.

 

• Consider additional pathway analysis or integration with other data types for a more comprehensive biological understanding.