Abstract
A comparison of five different measures of pathway expression and a public map of pathway expression in human tissues are presented.
Highlights
Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists
A global view of the behavior of defined biological modules is more intuitive than expression levels of hundreds or thousands of individual genes, and biological interpretation is much faster than analyzing a list of genes with significant expression changes because functionally understood gene sets with significant expression changes are identified automatically
Analysis techniques normally used at the gene-level to analyze individual genes such as correlation, clustering, and analysis of variance may be leveraged to analyze gene sets and offer additional insights
Summary
Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists. The main result of a microarray experiment is a list of genes whose expression is significantly changed relative to a comparison sample. This gene list will typically contain hundreds to thousands of genes, and biological interpretation of this list is often the most time-consum-. Most of the genes in the list are grouped and understood in terms of biological processes that have meaning to the scientist, such as the activation or repression of particular pathways or sets of genes with common function. Recent increases in available gene annotation and pathway databases have made it possible and worthwhile to complement this manual approach with automated analysis of pathway expression changes, the coordinated induction or repression of multiple genes in a predefined pathway, by reference to a database of known pathways. We use the terms 'pathway' and 'gene set' interchangeably
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