KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model

Area of Science:

• Bioinformatics
• Systems Biology
• Transcriptomics

Background:

• Traditional overrepresentation analysis (ORA) and dynamic impact approach (DIA) are used for functional analysis of gene expression data.
• DIA estimates the biological impact of differentially expressed genes (DEGs) but overlooks the hierarchical correlations within pathway databases like Kyoto Encyclopedia of Genes and Genomes (KEGG).

Purpose of the Study:

• To address the limitations of DIA by developing a path analysis model (KEGG-PATH) that accounts for inter-pathway correlations.
• To subdivide the effects of KEGG pathways into direct and indirect components.
• To estimate the impact direction of KEGG pathways using principal component analysis (PCA).

Main Methods:

• Development of the KEGG-PATH model incorporating path analysis.
• Integration of correlation structures between KEGG pathways.
• Application of gradient analysis via PCA for impact direction estimation.
• Functional analysis of bovine mammary transcriptome data during lactation.

Main Results:

• The KEGG-PATH model successfully subdivides pathway effects into direct and indirect components, considering pathway interdependencies.
• Preliminary estimation of impact direction for KEGG pathways was achieved using PCA-based gradient analysis.
• The model demonstrated its advantage in analyzing the functional landscape of the bovine mammary transcriptome during lactation.

Conclusions:

• KEGG-PATH offers a more sophisticated approach to functional enrichment analysis by modeling pathway correlations.
• This method provides deeper insights into biological mechanisms compared to standard DIA, especially in complex datasets like time-course transcriptomics.
• The KEGG-PATH model is a valuable tool for dissecting complex biological systems and understanding gene function dynamics.