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Why would some genes have more than two modes in their expression distribution? What external factors would cause this anomaly?

I'm referring to the expression distribution of a gene across different tissue samples. For example, if one was to download a bunch of data from NCBI GEO, and pinpoint one gene and plot the expression level versus the frequency for that gene across all those data sets, some genes would have more than 2 modes (2 expression levels with very high frequency). This is the only case I'm interested in: more than 2 modes - not bimodal. So what would cause more than two modes?

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  • $\begingroup$ Could you expand please? Expression distribution over what? The same tissue? Same cell over time? Different tissues? $\endgroup$ – nico Jan 19 '15 at 1:08
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    $\begingroup$ @nico's point is spot on, we can't answer until we know more. Also, should I intepret your question to mean you are not interested in bimodal (i.e exactly 2 modes) distributions? Those are usually easier to explain, have more concrete examples, and should generalize to a multimodal case. But if you are not interested in bimodality please make that clear. $\endgroup$ – A. Kennard Jan 19 '15 at 3:54
  • $\begingroup$ My original question was a bit vague - here are some clarifications. I'm referring to the expression distribution of a gene across different tissue samples. For example, if one were to download a bunch of data from NCBI GEO, and pinpoint one gene and plot the expression level versus the frequency for that gene across all those data sets, some genes would have more than 2 modes (2 expression levels with very high frequency). As I alluded to in my post, this is the only case I'm interested in: more than 2 modes - not bimodal. So what would cause more than two modes? $\endgroup$ – swetharevanur Jan 19 '15 at 4:45
  • $\begingroup$ @newintern Edit your question instead of clarifying in the comments. $\endgroup$ – WYSIWYG Jan 19 '15 at 4:49
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One trivial situation in which this can happen is when the tissue used for expression studies is heterogeneous. Different cells express different levels of the gene.

Bimodality can be observed when the system can actually occupy two stable states; i.e. a gene can either have a high expression or a low expression. When you sample the population you would get two peaks. Bistablity (two stable steady states) is a common phenomenon in biological systems and positive feedbacks generally exhibit such behavior. In bistable systems there is also an unstable steady state which lies "between" the two stable states (like a mountain separating two valleys). If the system is in the unstable state, it can fall to either of the two stable states. (See this article for an example). This concept can be extended to multistable systems but they are a little more complex than the simple feedbacks. However they can theoretically exist (I don't know of a biological example yet).

Bimodality/multimodality can be also observed in the absence of a deterministic bistability in the system. This happens because of the expression noise due to stochasticity and is observed in case of transcription bursts (See here).

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  • $\begingroup$ Sure - different cells can express different levels of the gene, but the frequencies will be relatively smaller, won't they? $\endgroup$ – swetharevanur Jan 19 '15 at 4:48
  • $\begingroup$ @newintern Depends on the parameters. They can be comparable as well. $\endgroup$ – WYSIWYG Jan 19 '15 at 4:49
  • $\begingroup$ Just saw your edit. If this raw expression data were clustered or smoothed to eliminate noise would transcription bursts still be significant enough to be considered another major peak? $\endgroup$ – swetharevanur Jan 19 '15 at 4:50
  • $\begingroup$ @newintern This is true biological noise not a technical noise because of the experiment. By removing it you are dismissing a natural phenomenon. And yes the bursts can give rise to significant effects. See the linked paper. $\endgroup$ – WYSIWYG Jan 19 '15 at 4:51
  • $\begingroup$ Alright I understand now. This will help me explicate my data. Thanks! $\endgroup$ – swetharevanur Jan 19 '15 at 4:52

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