Geodesic
Significance tests to identify regulated proteins based on a large number of small samples
Kybernetika, Tome 48 (2012) no. 3, pp. 478-493.

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Modern biology is interested in better understanding mechanisms within cells. For this purpose, products of cells like metabolites, peptides, proteins or mRNA are measured and compared under different conditions, for instance healthy cells vs. infected cells. Such experiments usually yield regulation or expression values – the abundance or absence of a cell product in one condition compared to another one – for a large number of cell products, but with only a few replicates. In order to distinguish random fluctuations and noise from true regulations, suitable significance tests are needed. Here we propose a simple model which is based on the assumption that the regulation factors follow normal distributions with different expected values, but with the same standard deviation. Before suitable significance tests can be derived from this model, a reliable estimation for the standard deviation in the context of many small samples is needed. We therefore also include a discussion on the properties of the sample MAD (Median Absolute Deviation from the median) and the sample standard deviation for small samples sizes.
Classification : 62A10, 93E12
Keywords: MAD; standard deviation; small samples; significance test 
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     author = {Klawonn, Frank},
     title = {Significance tests to identify regulated proteins based on a large number of small samples},
     journal = {Kybernetika},
     pages = {478--493},
     publisher = {mathdoc},
     volume = {48},
     number = {3},
     year = {2012},
     mrnumber = {2975802},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2012__48_3_a8/}
}
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Klawonn, Frank. Significance tests to identify regulated proteins based on a large number of small samples. Kybernetika, Tome 48 (2012) no. 3, pp. 478-493. http://geodesic.mathdoc.fr/item/KYB_2012__48_3_a8/