{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Identification of Differential Gene Pathways in Microarray Data","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/76527"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"A gene pathway typically refers to a group of genes and small molecules that work together to control one or more cell functions. In systems biology, pathway analysis is of paramount biological importance, and recent studies revealed that malfunction of gene pathways could induce disease manifestations, such as cancer. Usually, a gene pathway consists of two components: the upstream factors, which are signaling molecules transmitting stimulus from cell surface to nucleus, and the downstream factors, which respond to cell signaling through changes of their expression levels. Although several methods have been reported for analysis of gene pathways, almost all of them focus on the upstream factors of a pathway, ignoring the rich information from the downstream factors. In this thesis work, we first investigated and compared the existing gene pathway analysis methods, particularly on three most popular ones: Gene Set Enrichment Analysis (GSEA), Principal Component Analysis (PCA), and Canonical Discriminant Analysis (CDA). We then proposed an innovative method based on the concept of integrating the statistical information from both upstream and downstream factors to infer differential gene pathways. More specifically, the Relax Intersection-Union Test (RIUT) framework was employed to combine evidences from upstream and downstream factors. We performed intensive simulation studies with GSEA, PCA and CDA. We found out both the limitations and strengths of these methods under various data structures, and we identified scenarios in which each method can outperform the others. Furthermore, we demonstrated that our proposed combining method outperforms the above existing methods in terms of both power and interpretability in biology. We applied the combining method to two real data sets: the p53 data set and Essential thrombocythaemia data set. The results suggest that in the combining method, GSEA is more appropriate for the upstream subgroup and CDA is more powerful for the downstream subgroup due to their distinct data structures."},{"label":"dcterms.available","value":"2017-09-20T16:50:33Z"},{"label":"dcterms.contributor","value":"Wu, Song"},{"label":"dcterms.creator","value":"Zhang, Qiao"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:50:33Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:50:33Z"},{"label":"dcterms.description","value":"Department of Applied Mathematics and Statistics."},{"label":"dcterms.extent","value":"130 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/76527"},{"label":"dcterms.issued","value":"2015-12-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:50:33Z (GMT). No. of bitstreams: 1\nZhang_grad.sunysb_0771E_12189.pdf: 4079656 bytes, checksum: 353080c853f2104ff34665a65c32a4ed (MD5)\n  Previous issue date:    1"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Statistics"},{"label":"dcterms.title","value":"Identification of Differential Gene Pathways in Microarray Data"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/91%2F73%2F02%2F91730256846118695776642135545338781110/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/91%2F73%2F02%2F91730256846118695776642135545338781110","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}