The heat map plot in shows the pattern of circadian expression in approximately 30% of all genes interrogated by the Affymetrix mouse expression microarray. At first glance the pattern of two red (zenith) and two green (nadirs) areas over two-day period is remarkably similar to the previously published circadian expression patterns in mice 
. However in this case none of the genes selected for analysis has been called “Present” even once at any of the 12 time points. This effect is not specific to a particular tissue and observed in all mouse and yeast data sets considered in this paper.
Circadian expression pattern in transcripts never called present.
In most studies such “silent” genes are excluded from further analysis on the early stages. The filtration criteria are usually more stringent, selecting only genes called “Present” in at least half of all time points 
. In the previous publications we have reported circadian oscillation in nearly 100% of all genes 
. But the oscillating pattern does not show a strong dependence on the absolute level of expression or any regard to the signal/background noise ratio of the Affymetrix GeneChip. shows relations between likeliness of circadian oscillation (estimated by a periodicity test p-value) and the overall median of expression signal in time series. There is no indication of a threshold associated with presence or absence call. Genes expressed below the noise level (typically with signal reading under 150) generate the same pattern as highly expressed genes. This finding is corroborated by the results of periodicity tests performed on the subset of non-present genes (see ). As expected, the number of “absent” genes for which a periodic pattern is observed with the confidence level of p<0.1
is lower and the expression profiles are generally noisier compared to analysis of entire set of transcripts 
. However, in spite of the lower signal to noise ratio the underlying baseline circadian, oscillation is detectable in majority of the profiles. This pattern and the proportion between phase groups are consistent with that of the “present” genes or the mixture of “present” and “absent” genes (transcripts). These observations lead to the conclusion that the criteria separating “present” from “absent” genes is arbitrary. The low signal emitted from the microarray probes can be below the noise level for the chip at each particular time point. However, it reflects the pattern of gene expression rather than an ambient noise. This point can be further illustrated by which shows expression pattern of transcripts called “Absent” and “Present” in the contexts of a biological pathway (a fragment of insulin signaling pathway, data from Metacore database, GeneGo Inc.). In spite of the absence call for the insulin receptor the white adipose tissue is known to respond to the insulin signal 
. The profile for the “Absent” insulin receptor is pronouncedly circadian and perfectly synchronized with production rate of the insulin receptor substrate, which is called present.
A scatter plot of mean intensity (axis X) and likeliness of periodicity estimated by Pt-test p-value (axis Y).
Numbers of circadially oscillating “absent” genes as reported by different algorithms and testing strategies.
Insulin regulation of lipid metabolism in white adipose tissue (fragment).
Periodic patterns observed in genes usually considered unexpressed are not necessarily associated with circadian rhythm. Analysis of respiratory oscillation pattern in S.cerevisae
reveals a large group of genes (or rather transcripts interrogated by Affymetrix probe sets) that demonstrate a clear oscillating pattern consistent with that of highly expressed genes (Supplemental Figure S2
Why do the “absent” genes, with expression pattern otherwise indistinguishable from the background noise suddenly show signs of expression consistent with that of reliably detectable genes? The explanation has been outlined in the abstract of the very first paper reporting the effect of SR 
. The paper had shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbation is absent. In microarray gene expression studies the threshold is defined by the signal and noise levels estimated from the luminescent signal read from the spot with immobilized probes for a specific gene (transcript) and the background luminescence from the space between spots and/or blank spots with no specific probe. The details of signal detection may vary, particularly with Affymetrix summation algorithms (see 
for review). However, regardless of the image processing and inference procedure microarray can be viewed as a detector with a certain threshold. All contemporary methods concentrate on improving the signal to noise ratio by either lowering the noise or amplifying the signal or both. However, the underlying periodicity of gene expression can provide an essential component for the SR to take effect. With known periodicity of the signal we have all necessary factors for SR. Periodicity of expression in nearly 100% of eukaryotic genes has been demonstrated in our recent paper 
. The dominating rhythm is circadian in case of murine peripheral tissues. Expression pattern in yeast (S. cerevisae
) is dominated by metabolic oscillation in respiratory cycle and this rhythm is also observed in nearly 100% of all genes (transcripts). However, the effect of SR can be achieved even without a natural baseline oscillation. Oscillation can be generated by repetitive application of perturbation (signal, treatment) in a biological system. Periodicity does not have to be time-wise. Regular placement of replicate probes on a lattice can also be viewed as a periodic signal across the surface of microarray and in combination with background noise it can create the effect of SR. In this case application of SR would require a new specifically designed microarray as well as significant modification of the analysis pipeline, starting from the image analysis on and between the spots of attached probes. In all cases the algorithm for detection of signal is based on a test for periodicity in a series of measurements rather than static comparison of signal and background noise levels.
Using the SR methodology, the test for gene silence can be formulated as follows: in presence of both periodic signal with known frequency ω
and stochastic noise the null hypothesis (H0
: gene Y is expressed) is equivalent to H0
: expression signal for gene Y is periodic with frequency ω
could not be rejected if test for periodicity is positive, alternative hypothesis (HA
: gene X is silent) is accepted if there is no evidence of oscillation with frequency ω
. There are a few available tests for periodicity; we suggest Pt-test 
, specifically developed for short time series with low sampling rate, typical for gene expression profiles. This test can be applied in conjunction with digital signal processing in phase continuum approach 
, which increases the test's ability to identify baseline oscillation. The concept of assigning detection calls using stochastic resonance is further illustrated by the computer program in supplemental materials (supplemental file Code S1
). This program implements the simplest variant of detection call assignment: if sliding frame in phase continuum tests positive for baseline oscillation the genes found in this frame are called “Present”. More sophistication can be added by testing the presence of the baseline oscillation by a panel of statistical tests and/or taking in account the number of adjacent frames testing positive or negative. Multiple testing of FDR adjustment in not applicable for the reasons explained in the Methods
Detection of the extra-low gene expression has a few important implications. Long term practice of using microarray and RT-PCR technology has created a perception that a gene for which signal has the same intensity as ambient noise is not expressed. However, this fact relates to the resolution ability of the method rather than a real property of the gene. Using the principle of SR we greatly improve our ability to detect weak signals, but this method also has its limit. We observe expression of a large number of genes previously considered silent, but again this signal sinks into noise with no clear landmark separating expressed and silent fractions of genes. Could it be that the latter fraction does not exist and all genes are expressed, even at a miniscule rate? The entire concept of “silent” genes is created by our inability to detect extremely low transcript concentrations. There is no obvious landmark separating low-expressed genes and below-detection-threshold genes. Summing up the number of conventionally detected transcripts and transcripts detectable by SR leaves a very small fraction of truly silent gene candidates. This fraction also contains transcripts for which microarray probes are not performing as intended, which further reduces the number of potentially silent genes almost to none. Recent publication has already demonstrated that most human protein-coding genes are primed for transcription initiation, including those for which no transcripts could be detected 
. Now we can detect those elusive transcripts with the new computational tools and a novel approach to the analysis of low-abundance transcripts. The “pilot light” suggested in the title seems to be more appropriate than “silent” for the genes expressed below the standard detection threshold. Such genes are likely to have transcription initiation complex in place, but no significant accumulation of mature transcripts in the cytoplasm. Theoretically, the concept of all genes being expressed, only at very different scale does not contradict the accumulated knowledge about cellular processes. However, ability to detect the extremely low expression and account for it in the experiment design opens new prospective for better, more complete understanding of the cellular processes, better account for potential adverse effects in medication and more precise biology in general.