ABSTRACT
Motivation
Periodic patterns in time series resulting from biological
experiments are of great interest. The commonly used Fast Fourier
Transform algorithm is applicable only when data are evenly spaced
and when no values are missing, which is not always the case in
high-throughput measurements. The choice of statistic to evaluate
the significance of the periodic patterns for unevenly spaced gene
expression time series has not been well substantiated.
Methods
The Lomb-Scargle periodogram approach is used to search time
series of gene expression to quantify the periodic behavior of every
gene represented on the DNA array. The Lomb-Scargle periodogram
analysis provides a direct method to treat missing values and
unevenly spaced time points. We propose the combination of a Lomb-Scargle test statistic for periodicity and a multiple hypothesis testing
procedure with controlled false discovery rate to detect significant
periodic gene patterns.
Results
We analyzed Bozdech's Plasmodium falciparum gene expression dataset.
In the Quality Control Dataset of 5080 expression patterns, we
found 4112 periodic probes. In addition, we identified 243 probes
with periodic expression in the Complete Dataset, which could not
be examined in the original study by the FFT analysis due to an
excessive number of missing values. While most periodic genes had
a period of about 48 hr, some had a period close to 24 hr. Our
approach should be applicable for detection and quantification of
periodic patterns in any unevenly spaced gene expression time series
data.
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