View - ResearchGate
View - ResearchGate View - ResearchGate
2Association Analysis for Large-Scale Gene Set DataStefan A. Kirov, Bing Zhang, and Jay R. SnoddySummaryHigh-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 isnecessary 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 thegenes with a particular biological feature, for example, a given pathway. Statistical analysis couldbe used to evaluate if a gene set is truly associated with a feature. Over the past few years manytools 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.Key Words: Association analysis; data interpretation; gene expression; gene set; WebGestalt;genome-scale; high-throughput analysis.1. IntroductionBecause of the first large-scale expression analysis in 1995 (1), numerousstudies have tried to correlate the observed expression patterns with other significantbiological data, such as phenotypes, regulatory sequences, pathways, andso on. Such types of correlation analysis could potentially reveal mechanismsthat are associated with the observed expression patterns. The results fromlarge-scale biological experiments, such as expression analysis is often complex.In many cases, it will not be possible to infer the aforementioned associationsby manual analysis because of the data size and complexity. An overview of themicroarray technology and some of the computer-assisted inference analyses isreviewed by Stoughton (2).A large number of studies use gene ontology (GO) annotation (3) to assist inthe analysis of gene expression data. For example, Bono et al. used GO toreconstruct metabolic pathways (4). The GO consortium (3) provides a powerfulFrom: Methods in Molecular Biology, vol. 408: Gene Function AnalysisEdited by: M. Ochs © Humana Press Inc., Totowa, NJ19
- Page 12: PrefaceThis volume of Methods in Mo
- Page 16: Prefaceixcolleagues demonstrate how
- Page 20: xiiContentsPART III EXPERIMENTAL ME
- Page 26: ICOMPUTATIONAL METHODS I
- Page 34: 4 BidautTable 1Input File Format Us
- Page 38: 6 BidautTable 2Folder Layout to Use
- Page 42: 8 Bidaut• alphaA: this is the num
- Page 46: 10 Bidautcomputing the maximum corr
- Page 50: 12 BidautFig. 3. The complete Clutr
- Page 54: Table 3Some Identified Patterns (5,
- Page 58: 16 BidautFig. 4. This is a comparis
- Page 62: 18 BidautReferences1. Hughes, T. R.
- Page 68: Table 1Tools That Can Perform Gene
- Page 72: Association Analysis for Large-Scal
- Page 76: Association Analysis for Large-Scal
- Page 80: Association Analysis for Large-Scal
- Page 84: Association Analysis for Large-Scal
- Page 88: Association Analysis for Large-Scal
- Page 92: Association Analysis for Large-Scal
- Page 96: 36 Wang and Ochsfunction (9). Herei
- Page 100: 38 Wang and Ochs1. Download the LS-
- Page 104: 40 Wang and OchsFig. 1. The PattRun
- Page 108: 42 Wang and OchsFig. 3. The PattRun
- Page 112: 44 Wang and OchsFig. 4. The gene ta
2Association Analysis for Large-Scale Gene Set DataStefan A. Kirov, Bing Zhang, and Jay R. SnoddySummaryHigh-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 isnecessary 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 thegenes with a particular biological feature, for example, a given pathway. Statistical analysis couldbe used to evaluate if a gene set is truly associated with a feature. Over the past few years manytools 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.Key Words: Association analysis; data interpretation; gene expression; gene set; WebGestalt;genome-scale; high-throughput analysis.1. IntroductionBecause of the first large-scale expression analysis in 1995 (1), numerousstudies have tried to correlate the observed expression patterns with other significantbiological data, such as phenotypes, regulatory sequences, pathways, andso on. Such types of correlation analysis could potentially reveal mechanismsthat are associated with the observed expression patterns. The results fromlarge-scale biological experiments, such as expression analysis is often complex.In many cases, it will not be possible to infer the aforementioned associationsby manual analysis because of the data size and complexity. An overview of themicroarray technology and some of the computer-assisted inference analyses isreviewed by Stoughton (2).A large number of studies use gene ontology (GO) annotation (3) to assist inthe analysis of gene expression data. For example, Bono et al. used GO toreconstruct metabolic pathways (4). The GO consortium (3) provides a powerfulFrom: Methods in Molecular Biology, vol. 408: Gene Function AnalysisEdited by: M. Ochs © Humana Press Inc., Totowa, NJ19