title: Association analysis for large-scale gene set data.
abstract: High-throughput experiments in biology often produce sets of genes of potential interests. Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pathway. Statistical analysis could be used to evaluate if a gene set is truly associated with a feature. Over the past few years many tools that perform such analysis have been created. In this chapter, using WebGestalt as an example, it will be explained in detail how to associate gene sets with functional annotations, pathways, publication records, and protein domains.
authors.affiliation: Oak Ridge National Laboratory, University of Tennessee, USA.
authors.forename: Stefan A
authors.initials: SA
authors.lastname: Kirov
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
authors.forename: Jay R
authors.initials: JR
authors.lastname: Snoddy
mesh_terms: D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005821:Genetic Techniques / Q000706:statistics & numerical data*; D023281:Genomics / Q000706:statistics & numerical data; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012984:Software