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Abstract:
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Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats and structures have become available in recent years. Among them are microarray gene expression measurements, Single Nucleotide Polymorphisms (SNPs), and Copy Number Variations (CNVs). Each of these distinct data types provides a different, partly independent and complementary view of the whole genome. However, understanding functions of genes and other aspects of the genome requires more information than provided by each of the data sets. We will present novel methods based on sparse canonical correlation analysis and kernel approaches. These methods can be used to extract signal from high-dimensional data and combine information across different data sets. We will provide illustrative practical examples and discuss methodological issues.
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