Density-based clustering analyses to identify heterogeneous cellular sub-populations

Tiffany M. Heaster; Alex J. Walsh; Bennett A. Landman; Melissa C. Skala

doi:10.1117/12.2252499

8 February 2017 Density-based clustering analyses to identify heterogeneous cellular sub-populations

Tiffany M. Heaster, Alex J. Walsh, Bennett A. Landman, Melissa C. Skala

Proceedings Volume 10043, Diagnosis and Treatment of Diseases in the Breast and Reproductive System; 100430X (2017) https://doi.org/10.1117/12.2252499
Event: SPIE BiOS, 2017, San Francisco, California, United States

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.

Citation Download Citation

Tiffany M. Heaster, Alex J. Walsh, Bennett A. Landman, and 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); https://doi.org/10.1117/12.2252499

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