8 February 2017 Density-based clustering analyses to identify heterogeneous cellular sub-populations
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tiffany M. Heaster, Alex J. Walsh, Bennett A. Landman, Melissa C. Skala, "Density-based clustering analyses to identify heterogeneous cellular sub-populations", Proc. SPIE 10043, Diagnosis and Treatment of Diseases in the Breast and Reproductive System, 100430X (8 February 2017); doi: 10.1117/12.2252499; https://doi.org/10.1117/12.2252499
PROCEEDINGS
8 PAGES


SHARE
Back to Top