Genome Data Exploration Using Correspondence Analysis.

Area of Science:

• Bioinformatics and Computational Biology
• Genomics and Genetics
• Data Mining and Pattern Recognition

Background:

• Advancements in sequencing technologies generate massive genomic and genotyping data.
• Effective methods are needed to extract biologically meaningful knowledge from large datasets.
• Correspondence analysis (CA) is an established exploratory method for analyzing two-way data tables.

Purpose of the Study:

• To review Correspondence Analysis (CA) and its application in handling high-dimensional genomic and genetic data.
• To demonstrate CA's utility with examples from species composition and ortholog analysis.
• To compare CA with Principal Component Analysis (PCA) for interpreting complex biological data.

Main Methods:

• Review of Correspondence Analysis (CA) principles and applications.
• Application of CA to amino acid compositions of viruses, phages, yeast, and fungi.
• Analysis of pairwise shared orthologs in yeast and fungal species proteomes.
• Comparative analysis of CA and Principal Component Analysis (PCA) using hominid genotyping data.

Main Results:

• CA revealed distinct segregations between yeasts and fungi, and between viruses and phages based on amino acid composition.
• Ortholog distribution analysis using CA clustered yeast and fungal species according to their phylogenetic relationships.
• CA provided more detailed insights into ancestral similarities between ancient hominids and modern human populations compared to PCA.
• CA demonstrated superior interpretability by directly linking individual patterns to characteristic variables.

Conclusions:

• Correspondence Analysis (CA) is a valuable tool for exploring high-dimensional biological data from genomic and genetic studies.
• CA offers effective pattern recognition, revealing biological insights and phylogenetic relationships.
• CA provides more interpretable results than PCA, facilitating a deeper understanding of complex datasets.