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Proceedings Paper

Density-based clustering analyses to identify heterogeneous cellular sub-populations
Author(s): Tiffany M. Heaster; Alex J. Walsh; Bennett A. Landman; Melissa C. Skala
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Paper Abstract

Autofluorescence microscopy of NAD(P)H and FAD provides functional metabolic measurements at the single-cell level. Here, density-based clustering algorithms were applied to metabolic autofluorescence measurements to identify cell-level heterogeneity in tumor cell cultures. The performance of the density-based clustering algorithm, DENCLUE, was tested in samples with known heterogeneity (co-cultures of breast carcinoma lines). DENCLUE was found to better represent the distribution of cell clusters compared to Gaussian mixture modeling. Overall, DENCLUE is a promising approach to quantify cell-level heterogeneity, and could be used to understand single cell population dynamics in cancer progression and treatment.

Paper Details

Date Published: 8 February 2017
PDF: 8 pages
Proc. SPIE 10043, Diagnosis and Treatment of Diseases in the Breast and Reproductive System, 100430X (8 February 2017); doi: 10.1117/12.2252499
Show Author Affiliations
Tiffany M. Heaster, Univ. of Wisconsin-Madison (United States)
Morgridge Institute for Research (United States)
Alex J. Walsh, National Research Council (United States)
Air Force Research Lab. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Melissa C. Skala, Univ. of Wisconsin-Madison (United States)
Morgridge Institute for Research (United States)


Published in SPIE Proceedings Vol. 10043:
Diagnosis and Treatment of Diseases in the Breast and Reproductive System
Melissa C. Skala; Paul J. Campagnola, Editor(s)

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