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Application of statistical tests in a phase continuum setting mitigates some of the critical limitations of periodicity testing and allows improved signal to noise ratio by digital signal processing [ 40].
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We performed a periodicity test to determine the TFA profiles that exhibit periodic behavior, and by combining the sets of transcription factors collected by these two methods, we identified 5 putative cell cycle-related regulators: Dal81, Hap2, Hir2, Mss11, and Rlm1.
We did not found evidence supporting rigid rules, such as the periodicity of distance (in the range of period tested).
This finding is corroborated by the results of periodicity tests performed on the subset of non-present genes (see Table 1).
Figure 2 shows relations between likeliness of circadian oscillation (estimated by a periodicity test p-value) and the overall median of expression signal in time series.
For the assessment of our validation scheme, a subset of 800 genes is used which was previously selected using a periodicity test [20] and was first studied by Rhein and Strimmer for gene network inference [1].
Subsequently, we applied the periodicity test of [ 38] to identify the probes associated with the day-night cycle.
F3 seeds that harbored CCR2::LUC were collected from these plants for periodicity tests, as described below.
The results of application of periodicity tests to individual gene expression profiles are given in Supplemental Table 2 (see inside Additional File 1).
Highly expressed transcripts have more favorite signal to noise ratio and less likely to produce false-positive results in periodicity tests.
In earlier publications exploring this methodology independent validation of expression profiles confirmed oscillation pattern for multiple genes that did not pass the periodicity test, with or without FDR adjustment [ 36].
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