Raw? Log-transformed? It almost certainly matters. The underlying model used by bimodalIndex (and by Mclust) is that of a mixture of normal distributions. On the raw-linear scale of most microarrays, the distributions are skewed. (In fact, the usual RMA background correction model is to view the data as a mixture of normal background with exponential noise.) I would expect that fitting a mixture-of-normals model to this data would almost always conclude that it was (at least) bimodal, with the long exponential tail representing one of the modes. Kevin Javier P?rez Florido wrote: > Dear Wolfgang, > Thanks for your reply. > The data I am going to test for bi-modalities are raw data, without > preprocessing. For this purpose I think it is ideal to use > bimodalIndex function from ClassDiscovery package. It tests for > bimodalities using the information-based BIC criterion. > I know that there are more quality metrics such as boxplots, MA plots, > NUSE, etc...The use of histograms is complementary to all of them and > all I need is something that says that, maybe, a CEL file isn't good > due to such bi-modalities, taking into account the rest of quality > metrics. > > Thanks again, > Javier > > > > On 30/03/2010 14:54, Wolfgang Huber wrote: >> Dear Javier >> >> note that the number of modes of a distribution >> - can depend on the normalisation (before or after >> log-transformation; or whether background correction was done and how) >> - is impossible to determine from a finite sample without further >> assumptions (essentially a smoothing bandwidth) >> >> Besides these (significant) practical difficulties, I am also >> doubtfulof the usefulness, in terms of sensitivity and specificity, >> of this criterion for array quality diagnostics. If you see two >> modes, they would most likely be associated with a covariate, such as >> row, column, spatial position on the array. Then, if you find that >> this co-variate is quality-relevant, then I would advise checking for >> significant effects of that covariate even on arrays where the >> distribution looks uni-modal. >> >> Best wishes >> Wolfgang >> >> Mar 29, 2010, alle ore 6:14 PM, Javier P?rez Florido >> >> >>> Dear list, >>> Histograms are usually used to check the quality of microarray >>> experiments. If there are bi-modalities in a particular array, it is a >>> candidate to exclude it from the experiment. It is easy to check >>> bi-modalities or multi-modalities visually, but I would like to know if >>> there is a way (using a statistical test or something) to check >>> multi-modalities using the data returned by the hist function. >>> >>> For an Affybatch object, hist function returns the X and Y values, but >>> that's all, it doesn't return the variables breaks, counts, etc as >>> it is >>> said in the help manual for hist. So, I have two questions: >>> >>> * Is there a test to check for multi-modalities in histograms? >>> * Is there a way to know the cells and the number of values per >>> cell >>> used by hist to check for multi-modalities in a rudimentary way? >>> >>> Thanks again, >>> Javier >>> >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor