  Appropriate preprocessing is a fundamental prerequisite for analyzing a noisy
dataset. The purpose of this paper is to apply a nonparametric preprocessing
method, called Singular Spectrum Analysis (SSA), to a variety of datasets which
are subsequently analyzed by means of multiple statistical hypothesis tests.
SSA is a nonparametric preprocessing method which has recently been utilized in
the context of many life science problems. In the present work, SSA is compared
with three other state-of-the-art preprocessing methods in terms of goodness of
denoising and in terms of the statistical power of the subsequent multiple
test. These other methods are either parametric or nonparametric. Our findings
demonstrate that (multivariate) SSA can be taken into account as a promising
method to reduce noise, to extract the main signal from noisy data, and to
detect statistically significant signal components.
