Dissertation/Thesis Abstract

Statistical methods in microarrays and high-throughput flow cytometry
by Meirelles, Osorio, Ph.D., The University of New Mexico, 2009, 177; 3390837
Abstract (Summary)

Background. Heterogeneous cell populations have previously been described as noisy. However, recent studies have demonstrated that heterogeneity can be biologically significant. We present here an approach for rapid and complete identification of heterogeneous cell populations from high-throughput flow cytometry data. We have developed a novel measure Slope Differentiation Identification (SDI) using flow cytometry-based protein expression, quantifying the rate of change in protein expression between two conditions (exponential and stationary phase) of yeast cells, as a function of cell size or cell granularity.

Results. SDI had superior Gene Ontology enrichment when compared with other approaches such as k-means clustering and an approach based on the bi-modality of the fluorescence intensity distribution. Cell populations were also validated using gradient-separation followed by microscopy, where proteins with high SDI measure showed significant levels of differentiation between high and low density cells.

Conclusion. Overall, our approach has identified novel protein expression patterns that differentiate quiescent and non-quiescent cell populations.

Indexing (document details)
Advisor: Werner-Washburne, Margaret
Commitee: Natvig, Donald, Toolson, Eric, Wearing, Helen
School: The University of New Mexico
Department: Biology
School Location: United States -- New Mexico
Source: DAI-B 71/02, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Biostatistics
Keywords: Calibration, Empirical Bayes, Flow cytometry, Gene correlation, Microarrays, Regression
Publication Number: 3390837
ISBN: 9781109612387
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest