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targets$Sample_Group)) # Fit the model for differential variability # specifying the intercept and age as the grouping factor fitvar http://www.selleckchem.com/products/RO4929097.html ( decideTests (fitvar)) Fleroxacin ## (Intercept) factor(age.targets$Sample_Group)OLD## -1 0 1325## 0 11441 393451## 1 417787 34452 topDV Luminespib manufacturer 4.783899 0.6970960 6.564472 0.0000000044240941## cg18160402 0.5624192 3.716228 0.5907985 6.520508 0.0000000053665347## Adj.P.Value## cg19078576 0.0001754857## cg11661000 0.0001754857## cg07065220 0.0001869984## cg05995465 0.0001937035## cg18091046 0.0001937035## cg05491001 0.0001937035## cg05542681 0.0001964159## cg02726803 0.0001964159## cg08362283 0.0002109939## cg18160402 0.0002303467 Similarly to the differential methylation analysis, is it useful to plot sample-wise beta values for the differentially variable CpGs to ensure the significant results are not driven by artifacts or outliers ( Figure 14). Figure 14. As for DMPs, it is useful to plot the top few differentially variable CpGs to check that the results make sense.