Gene expression profiling can produce effective biomarkers that can provide additional information beyond other approaches for characterizing disease. While these approaches are typically performed on standard bulk RNA sequencing data, new methods for RNA sequencing of individual cells have allowed these approaches to be applied at the resolution of a single cell. As these methods enter the mainstream, there is an increased need for user-friendly software that allows researchers without experience in bioinformatics to apply these techniques. In this thesis, I have developed new, user-friendly data resources and software tools to allow researchers to use gene expression signatures in their own datasets. Specifically, I created the Single Cell Toolkit, a user-friendly and interactive toolkit for analyzing single-cell RNA sequencing data and used this toolkit to analyze the pathway activity levels in breast cancer cells before and after cancer therapy. Next, I created and validated a set of activated oncogenic growth factor receptor signatures in breast cancer, which revealed additional heterogeneity within public breast cancer cell line and patient sample RNA sequencing datasets. Finally, I created an R package for rapidly profiling TB samples using a set of 30 existing tuberculosis gene signatures. I applied this tool to look at pathway differences in a dataset of tuberculosis treatment failure samples. Taken together, the results of these studies serve as a set of user-friendly software tools and data sets that allow researchers to rapidly and consistently apply pathway activity methods across RNA sequencing samples.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/34809 |
| Date | 21 February 2019 |
| Creators | Jenkins, David |
| Contributors | Johnson, W. Evan |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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