petal is a network analysis method that includes and takes advantage of precise Mathematics, Statistics, and Graph Theory, but remains practical to the life scientist. petal is built upon the assumption that large complex systems follow a scale-free and small-world network topology. One main intention of creating this program is to eliminate unnecessary noise and imprecision introduced by the user. Consequently, no user input parameters are required, and the program is designed to allow the two structural properties, scale-free and small-world, to govern the construction of network models.
The program is implemented in the statistical language R and is freely available as a package for download. Its package includes several simple R functions that the researcher can use to construct co-expression networks and extract gene groupings from a biologically meaningful network model. More advanced R users may use other functions for further downstream analyses, if desired.
The petal algorithm is discussed and its application demonstrated on several datasets. petal results show that the technique is capable of detecting biologically meaningful network modules from co-expression networks. That is, scientists can use this technique to identify groups of genes with possible similar function based on their expression information.
While this approach is motivated by whole-system gene expression data, the fundamental components of the method are transparent and can be applied to large datasets of many types, sizes, and stemming from various fields.
|Advisor:||Schlauch, Karen A.|
|Commitee:||Cushman, John C., Harris, Frederick C., Parchman, Thomas, Tittiger, Claus|
|School:||University of Nevada, Reno|
|School Location:||United States -- Nevada|
|Source:||DAI-B 78/07(E), Dissertation Abstracts International|
|Subjects:||Biostatistics, Biomedical engineering, Bioinformatics|
|Keywords:||Complex networks, Parammeter-free algorithm, Scale-free, Small-world, Whole-omics approach|
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