Health Research and Policy

Abstract

DATE: October 25, 2012
TIME: 1:15 - 3:00 pm
LOCATION: Li Ka Shing Center for Learning (LKSC)
291 Campus Dr, Room LK 209
1:15 pm - 3:00 pm
TITLE: Automated Identification of Stratifying Signatures in Cellular Sub-Populations
SPEAKER: Robert Bruggner
PhD student, Stanford Biomedical Informatics Training Program

Elucidation and examination of cellular subpopulations populations that display condition-specific behavior can play a useful role in disease diagnosis and prognosis, as well as providing a focal point for investigation of disease mechanism. Despite recent advancements in single-cell measurement technologies, identification of such relevant subpopulations through labor-intensive and non-exhaustive manual efforts remain commonplace. To address issues of scalability and subjectivity inherent in manual analyses, we developed Citrus (cluster identification, characterization, & regression), an automated approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. We demonstrate Citrus through the analysis of two mass cytometry data sets, recovering known biological signal in stimulated peripheral blood mononuclear cells and identifying novel populations of interest in primary ovarian cancer samples. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will enable investigators to perform thorough and unbiased inspection of cellular populations nested within high-dimensional datasets.

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