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1. 1. [preprocessQuantile] Fixing outliers. ## Warning in .getSex(CN = CN, xIndex = xIndex, yIndex = Luminespib molecular weight yIndex, cutoff## = cutoff): An inconsistency was encountered while determining sex. One## possibility is that only one sex is present. We recommend further checks,## for example with the plotSex function. ## [preprocessQuantile] Quantile normalizing. # create a MethylSet object from the raw data for plotting mSetRaw Fleroxacin sampGroups= targets$Sample_Group, main= ""Raw"" , legend= FALSE ) densityPlot ( getBeta (mSetSq), sampGroups= targets$Sample_Group, main= ""Normalized"" , legend= FALSE ) Data exploration Multi-dimensional scaling (MDS) plots are excellent for visualising data, and are usually some of the first plots that should this website be made when exploring the data. MDS plots are based on principle components analysis and are an unsupervised method for looking at the similarities and differences between the various samples. Samples that are more similar to each other should cluster together, and samples that are very different should be further apart on the plot. Dimension one (or principle component one) captures the greatest source of variation in the data, dimension two captures the second greatest source of variation in the data and so on. Colouring the data points or labels by known factors of interest can often highlight exactly what the greatest sources of variation are in the data. It is also possible to use MDS plots to decipher sample mix-ups.