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I am CS major, please be patient if my question is not well-stated.

The dataset is quantitative mass spectrometry (MS) data. By labeling proteins of two different samples A and B, we get the relative abundance of 100 to thousands of proteins in A/B. Alongside with this ratio, we can estimate its variance based on the signal intensities.

Wanted: A list of proteins significantly different from the set of all protein ratios.

Most proteins remain unchanged between A and B. The population of log-ratios distributes around 1. The histogram shows a bell shape with fat tails. Two-term Gaussian mixture model has been found to provide a good fit to experimental noise. I suppose it would work good for this data - think of experimental and biological noise.

How to test for significantly different ratios on such a two-term Gaussian mixture model?

Thanks for your responses!

  • 1
    You might want to cross-post this to the [stats site](http://stats.stackexchange.com/).2010-11-05
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    Thanks for the hint. I cross-posted on stats: http://stats.stackexchange.com/questions/4247/hypothesis-testing-on-mixture-models2010-11-05
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    Definitely this is more of a question for the stats site IMO. What error rate are you trying to control exactly? This sounds like the sort of problem where people typically try to control FDR, in which case I'm not sure how you implement it for a mixture model.2011-10-05

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