Monitoring of significant changes over time in fluorescence microscopy imaging of living cells
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Date
2015
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Abstract
The question whether structural changes in time-resolved images are of statistical
significance, and therefore of scientific interest, or merely emerge from random noise is of
great relevance in many practical applications such as live cell
uorescence microscopy,
where intracellular diffusion processes are investigated.
In this paper the statistical recovery of such time-resolved images from
fluorescence
microscopy of living cells is discussed, based on which a bootstrap method is introduced
that allows to both monitor and visualize statistically significant structural changes between
individual frames over time. The method can be adopted for use in other imaging
systems. It yields a criterion to assess time-resolved small scale structural changes e. g.
in the nanometer range.
The proposed bootstrap method is based on data reconstruction with a regularization
technique as well as new theoretical results on uniform confidence bands for the function
of interest in a two-dimensional heteroscedastic nonparametric convolution-type inverse
regression model of Poisson-type.
Moreover, a data-driven selection method for the regularization parameter based on
statistical multiscale methods is discussed. The method can be used for an automatic,
data-driven data analysis.
The theoretical results are demonstrated in a simulation study and are used to analyze
data of
fluorescently labeled intracellular transport compartments in living cells.
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Keywords
bootstrap, live cell microscopic imaging, fluorescence microscopy, deconvolution, confidence bands