Multicolor flow cytometry for healthy and uveitides patients

Multicolor flow cytometry datasets from 22 fresh peripheral blood patient samples. Please see attached ReadMe file

Quantitative multiparameter phenotyping of healthy and HGPS cells through cell averaging (“supercells”) and machine learning.

<p>A. Probability density distributions for one shape parameter (number of invaginations of the nuclear boundary) for healthy and diseased cell lines: (<b>i</b>) single cells; (<b>ii</b>) supercells of size 30. The cell averaging procedure removes the overlap between healthy and diseased cell line distributions. <b>B.</b> Distance from the perceptron boundary after machine learning, where positive (negative) distances correspond to the boundary side identified with the healthy (diseased) class: (<b>i</b>) single cells; (<b>ii</b>) supercells of size 30. Each cell line is shown separately along the horizontal axis. <b>C.</b> (<b>i</b>) Perceptron amplitudes: components of the vector normal to the classification hyperplane, each one associated with one of the shape parameters shown in the list. (<b>ii</b>) Fraction of cells correctly classified by the machine learning process as a function of the supercell size for a varying number of parameters used, as indicated. The top M measures are selected from the rank-ordered list based on the absolute values of the perceptron amplitudes.</p