Pathway Analysis of Microarray Data

During the past decade remarkable new techniques for transcriptional profiling have been developed. These include transcriptional profiling using hybridization microarrays as well as methods to sequence transcribed RNAs. No matter which technology is used, these experiments generate data on thousands of genes across multiple conditions and therefore the analysis of these data is often a daunting task. One of the most promising avenues for interpreting large datasets of expression profiles involves pathway-based analysis. Although pathway analysis of expression data is a relatively new field, many important advances have been made over the past few years. Below we outline some the most significant developments in this area of research. Introduction Pathways are collections of genes and proteins that perform a well-defined biological task. For instance, proteins that work to successively synthesize metabolites within a cell are grouped into metabolic pathways. Similarly, proteins that are involved in the transduction of a signal from the cell membrane to the nucleus are grouped into signal transduction pathways. These pathways have been established through decades of molecular biology research and are collected in a variety of public pathway repositories (Kanehisa et al., 2004; Ashburner et al., 2000). Since the number of known pathways within cells is significantly smaller than the number of genes that is typically profiled, the transformation of data from a gene-centric view to a pathways-centered one represents a dramatic reduction in the number of dimensions. Such a reduction allows a biologist to interpret and understand the data in a manner that is not possible when it is viewed as a collection of individual genes. Although pathway analysis of expression data is a relatively new field, many important advances have been made over the past few years. Below we outline some the most significant developments in this area of research. These include analyses that attempt to identify the pathways that are overrepresented among significantly perturbed genes in an experiment along with methods that attempt to identify pathways and networks of molecular interactions directly from expression data. Despite the fact that these analyses will undoubtedly continue to evolve rapidly over the next few years, they have already enhanced our ability to understand the biology that underlies complex experiments.