Spectral analysis of flow cytometric data: Design of a special‐purpose low‐pass digital filter

Abstract

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. Copyright © 1987 Wiley‐Liss, Inc.