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Abstract

An approach for quantification of an underlying property of a multitude of samples, e.g. cellular biological samples, is described. In an exemplary application, input measures are mean pixel intensities from different segments of individual cells on original and filtered images. In another exemplary application, input measures are pixel intensities from different cells on a series of time-gated images. The set of input measures are expected to have a linear relationship to the underlying property. Latent variables are (1) a scalar noise factor responsible for intensity fluctuations in all segments of a cell at a time and (2) the main underlying property of the process responsible for observed changes on images. Linear coefficients for latent variables are determined using noise minimization while constraining the mean signal from positive and negative control samples to the corresponding theoretical values. The method is well suited for high throughput applications like drug screening.

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First Claim