How to Interpret KEGG Analysis Results?
Following KEGG analysis, a results table and a pathway diagram are generated. These outputs provide critical insights into the functional significance of the identified differentially expressed genes.
Understanding the KEGG Pathway Table
1. Pathway Name
Lists the biological pathway name associated with the gene set.
2. Pathway ID
Provides a unique identifier for each pathway (e.g., hsa04110, corresponding to the human cell cycle pathway).
3. p-value
Represents the statistical test result, reflecting the significance of the association between the gene set and a specific pathway. A lower p-value indicates a stronger association.
4. Count
Specifies the number of genes from the input list that are mapped to the given pathway.
5. Gene List or Ratio
Displays either the specific genes associated with the pathway or the proportion of identified genes relative to the total number of genes within the pathway.
Interpreting the KEGG Pathway Diagram:
1. Color Coding
Genes or proteins from the input list that are present in a given pathway are highlighted using specific colors. For example, red may indicate genes identified in the dataset.
2. Graphical Representation
Arrows, lines, and other symbols illustrate molecular interactions and regulatory relationships. For instance, an arrow may indicate an activation relationship between two proteins.
Key Considerations for Interpretation
1. Statistical Significance
The p-value should be examined to identify pathways significantly associated with the gene set. Additionally, false discovery rate (FDR) correction is commonly applied to adjust for multiple comparisons and reduce false-positive findings.
2. Key Genes in Pathways
The highlighted regions in the KEGG pathway diagram correspond to genes within the dataset that are mapped to the pathway. These genes are crucial for understanding the biological function of the pathway in the context of the study.
This systematic approach ensures accurate interpretation of KEGG analysis results, facilitating a deeper understanding of gene function and pathway interactions.
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