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

Loading...
Thumbnail Image

Date

2013

Authors

Frelinger, Jacob Jeffrey

Advisors

Chan, Cliburn

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

379
views
329
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


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.