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A comparison of several tools for ontological analysis
| Content Provider | Scilit |
|---|---|
| Author | Draghici, Sorin |
| Copyright Year | 2016 |
| Description | Independently of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of genes found to be differentially expressed.1 The common challenge faced by the researchers is to translate such lists of differentially regulated genes into a better understanding of the automatic ontological analysis approach discussed in Chapter 23. Currently, this over-representation (ORA) approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. Since 2001 when the first such tool appeared, over a dozen other tools have been proposed for this type of analysis and more tools continue to appear every day (see Fig. 25.1). Although these tools use the same general approach, they differ greatly in many respects that influence in an essential way the results of the analysis. In most cases, researchers using such tools are either unaware of, or confused about certain crucial features. Book Name: Statistics and Data Analysis for Microarrays Using R and Bioconductor |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2009-0-01898-2&isbn=9780429130588&doi=10.1201/b11566-30&format=pdf |
| Ending Page | 928 |
| Page Count | 32 |
| Starting Page | 897 |
| DOI | 10.1201/b11566-30 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2016-04-19 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Statistics and Data Analysis for Microarrays Using R and Bioconductor Mathematical Psychology Tools Result Researchers Cases Lists Ontological Analysis Using Differentially |
| Content Type | Text |
| Resource Type | Chapter |
