Mason Archival Repository Service

Proteome-Transcriptome Alignment of Molecular Portraits by Self-Contained Gene Set Analysis: Breast Cancer Subtypes Case Study

Show simple item record

dc.contributor.advisor Baranova, Ancha
dc.contributor.author Ayaluri, Koushik
dc.creator Ayaluri, Koushik
dc.date 2020-05-27
dc.date.accessioned 2021-09-15T13:38:32Z
dc.date.available 2021-09-15T13:38:32Z
dc.identifier.uri http://hdl.handle.net/1920/12030
dc.description.abstract Gene sets are formed by grouping together functionally related genes or pathways. Gene set analysis (GSA) is a method previously developed for examining transcriptome data. As the gene sets are unit of expression in transcriptome-level GSA, similarly, the unit of protein abundance may be used for proteomics GSA. Self-contained and Competitive are two GSA approaches which differ by their underlining null hypothesis. In Self-contained approach, each gene set is evaluated to check if it is expressed differentially between two phenotypes. In Competitive approach, each gene set is compared to all the genes except the genes in that set. Competitive approaches are rapidly becoming popular for analyzing proteomics data, as much as they were for transcriptomics data. This research applied Self-contained GSA test of Gene sets net correlations analysis (GSNCA) to proteomics data of 77 annotated samples of breast cancers. Regardless of significant variation in the structure of proteomics and transcriptomics data, many pathway-wide characteristics features of breast cancer molecular subtypes were replicated at the protein level. In this work, GSA yielded a set of observations visible at proteome level, such as mitotic cell cycle process involvement in the HER2 molecular subtype. Overall, this study proves the value of Gene Sets Net Correlation Analysis (GSNCA) approach as a critical tool for analyzing proteomics data in general, and for dissecting protein-level molecular portraits of breast cancer tumors, in particular. en_US
dc.language.iso en en_US
dc.subject gene set analysis en_US
dc.subject proteome transcriptome en_US
dc.subject self-contained analysis en_US
dc.subject transcriptome alignment en_US
dc.subject breast cancer en_US
dc.title Proteome-Transcriptome Alignment of Molecular Portraits by Self-Contained Gene Set Analysis: Breast Cancer Subtypes Case Study en_US
dc.type Thesis en_US
thesis.degree.name Master of Science in Bioinformatics and Computational Biology en_US
thesis.degree.level Master's en_US
thesis.degree.discipline Bioinformatics and Computational Biology en_US
thesis.degree.grantor George Mason University en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search MARS


Browse

My Account

Statistics