Browsing by Author "Slater, John H."

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    Progeny Clustering: A Method to Identify Biological Phenotypes
    (Nature Publishing Group, 2015) Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.
    Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset.
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    Recapitulation and Modulation of the Cellular Architecture of a User-Chosen Cell of Interest Using Cell-Derived, Biomimetic Patterning
    (American Chemical Society, 2015) Slater, John H.; Culver, James C.; Long, Byron L.; Hu, Chenyue W.; Hu, Jingzhe; Birk, Taylor F.; Qutub, Amina A.; Dickinson, Mary E.; West, Jennifer L.
    Heterogeneity of cell populations can confound population-averaged measurements and obscure important findings or foster inaccurate conclusions. The ability to generate a homogeneous cell population, at least with respect to a chosen trait, could significantly aid basic biological research and development of high-throughput assays. Accordingly, we developed a high-resolution, image-based patterning strategy to produce arrays of single-cell patterns derived from the morphology or adhesion site arrangement of user-chosen cells of interest (COIs). Cells cultured on both cell-derived patterns displayed a cellular architecture defined by their morphology, adhesive state, cytoskeletal organization, and nuclear properties that quantitatively recapitulated the COIs that defined the patterns. Furthermore, slight modifications to pattern design allowed for suppression of specific actin stress fibers and direct modulation of adhesion site dynamics. This approach to patterning provides a strategy to produce a more homogeneous cell population, decouple the influences of cytoskeletal structure, adhesion dynamics, and intracellular tension on mechanotransduction-mediated processes, and a platform for high-throughput cellular assays.