|
|
@@ -0,0 +1,29 @@
|
|
|
+This is a series of diagnostic plots that were used to evaluate how
|
|
|
+well a particular statistical model fits the data and explains the
|
|
|
+sources of variation in an Illumina 450k dataset.
|
|
|
+
|
|
|
+- [`mean-var-model.pdf`](mean-var-model.pdf) shows the variance trend
|
|
|
+ modeling performed by voom, a method originally designed for
|
|
|
+ mean-variance modeling in RNA-seq data. In this case, it models the
|
|
|
+ mean-variance dependency induced by the logistic transform used for
|
|
|
+ converting beta values (i.e. percent methylation) to M-values (i.e.
|
|
|
+ ratio of methylated to unmethylated signal) in methylation data.
|
|
|
+ Page 2 shows the mean-variance trend after fitting the model with
|
|
|
+ the voom weights to cancel out the trend.
|
|
|
+- [`sample-weights.pdf`](sample-weights.pdf) Shows the results of
|
|
|
+ limma's `arrayWeights` method, which detects and down-weights
|
|
|
+ outlier samples, plotted against all known clinical covariates for
|
|
|
+ those samples. Diabetes status had a significant association with
|
|
|
+ the sample weights, indicating that the Type I diabetes samples were
|
|
|
+ overall more consistent and had fewer outlier observations that Type
|
|
|
+ II diabetes samples.
|
|
|
+- [`pcoa.pdf`](pcoa.pdf) shows a Principle Coordinate Plot (similar to
|
|
|
+ a PCA plot) of all the samples after subtracting out the effects of
|
|
|
+ known covariates. Points are sized by their sample weight, and a
|
|
|
+ crosshair shows the center of mass of each group.
|
|
|
+- [`pval-histograms.pdf`](pval-histograms.pdf) and
|
|
|
+ [`pval-cdf.pdf`](pval-cdf.pdf) show the p-value distributions for
|
|
|
+ each contrast of interest, presented as a histogram and as an
|
|
|
+ empirical cumulative distribution function. Each is annotated with
|
|
|
+ asymptotes indicating the estimated fraction of probes affected by
|
|
|
+ that contrast.
|
