Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry

Loading...
Thumbnail Image

Date

2013

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

381
views
341
downloads

Abstract

Automated analysis techniques for flow cytometry data can address many of the limitations of manual analysis by providing an objective approach for the identification of cellular subsets. While automated analysis has the potential to significantly improve automated analysis, challenges remain for automated methods in cross sample analysis for large scale studies. This thesis presents new methods for data normalization, sample enrichment for rare events of interest, and cell subset relabeling. These methods build upon and extend the use of Gaussian mixture models in automated flow cytometry analysis to enable practical large scale cell subset identification.

Description

Provenance

Citation

Citation

Frelinger, Jacob Jeffrey (2013). Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/8213.

Collections


Except where otherwise noted, student scholarship that was shared on DukeSpace after 2009 is made available to the public under a Creative Commons Attribution / Non-commercial / No derivatives (CC-BY-NC-ND) license. All rights in student work shared on DukeSpace before 2009 remain with the author and/or their designee, whose permission may be required for reuse.