Spectral decomposition of flow cytometric datafiles of arbitrary dimension reveal information of both the signal and the noise components that constitute the histograms. This spectral information is used to construct a low-pass digital filter, which removes the high-frequency noise from the actual data. It is shown that this procedure guarantees nontrivial smoothing of the flow cytometric data in accordance with the local experimental situation. As a consequence optimal reconstruction of the signal is possible, which facilitates unambigous interpretation of the data files and mathematical estimation of the statistical parameters.
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