Integrative Exploratory Analysis of Two or More Genomic Datasets

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Exploratory analysis is crucial for high-throughput and multi-omics data.
• Multivariate methods like CA, PCA, and MDS are standard for single datasets.
• Modern biological studies generate multiassay data (e.g., mRNA, protein), necessitating integrative analysis.

Purpose of the Study:

• To present co-inertia analysis (CIA) for two datasets and multiple co-inertia analysis (MCIA) for three or more datasets.
• To demonstrate how these multivariate methods facilitate the exploratory analysis of multiple omics data.
• To highlight their utility in identifying correlated structures and facilitating biological interpretation.

Main Methods:

• Application of co-inertia analysis (CIA) for analyzing two datasets.
• Application of multiple co-inertia analysis (MCIA) for analyzing three or more datasets.
• Utilizing graphical representations and simultaneous projection of samples and variables onto a lower-dimensional space.

Main Results:

• CIA explored concordance between mRNA and protein expression in NCI-60 tumor cell lines.
• MCIA performed cross-platform comparison of mRNA expression data from four microarray platforms.
• Integrated transcriptomic, proteomic, and phosphoproteomic data from stem cell lines were analyzed.

Conclusions:

• CIA and MCIA are powerful, simple multivariate approaches for integrative exploratory analysis of high-dimensional omics data.
• These methods effectively represent samples in a lower-dimensional space, simplifying the identification of inter-dataset correlations.
• Simultaneous projection of samples and variables aids in selecting key variables for biological interpretation and pathway analysis.