Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput
measurement devices are available, such as microarrays and
sequencing techniques, regulatory networks have been intensively studied
over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing
regression models and apply them to construct regulatory networks which
span trancription factors and microRNAs. We also propose an extended
algorithm to address the local optimum issue in finding the Maximum A
Posterjorj estimator. An E. coli mRNA expression microarray data set with
known bona fide interactions is used to evaluate our models and we show
that our regression networks with a properly chosen prior can perform comparably
to the state-of-the-art regulatory network construction algorithm.
Finally, we apply our models on a p53-related data set, NCI-60 data. By
further incorporating available prior structural information from sequencing
data, we identify several significantly enriched interactions with cell proliferation
function. In both of the two data sets, we select specific examples
to show that many regulatory interactions can be confirmed by previous
studies or functional enrichment analysis. Through comparing statistical
models, we conclude from the project that combining different models with
over-representation analysis and prior structural information can improve
the quality of prediction and facilitate biological interpretation.
Keywords: regulatory network, variable selection, penalized maximum
likelihood estimation, optimization, functional enrichment analysis.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU./4068 |
| Date | 11 1900 |
| Creators | Chen, Xiaohui |
| Publisher | University of British Columbia |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | 2223158 bytes, application/pdf |
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