nCCl4 quality metrics report

Figure 1 represents MA plot for each array.
MA plots are useful for pairwise comparisons between arrays. M and A are defined as :
M = log₂(I₁) - log₂(I₂)
A = 1/2 (log₂(I₁)+log₂(I₂))
where I₁ and I₂ are the vectors of intensities of two channels. Typically, we expect the mass of the distribution in an MA plot to be concentrated along the M = 0 axis, and there should be no trend in the mean of M as a function of A. Note that a bigger width of the plot of the M-distribution at the lower end of the A scale does not necessarily imply that the variance of the M-distribution is larger at the lower end of the A scale: the visual impression might simply be caused by the fact that there is more data at the lower end of the A scale. To visualize whether there is a trend in the variance of M as a function of A, consider plotting M versus rank(A).

Figure 2: False color representations of the arrays' spatial distributions of feature intensities and, if available, local background estimates. The color scale is shown in the panel on the right, and it is proportional to the ranks. These plots may help in identifying patterns that may be caused, for example, spatial gradients in the hybridization chamber, air bubbles, spotting or plating problems.

Figure 3 presents boxplots. On the left panel, the green boxes correspond to the log₂ intensities of the green channel. On the middle panel the red boxes correspond to the log₂ intensities of the red channel. The right panel shows the boxplots of log₂(ratio).

Figure 4 shows density estimates (histograms) of the data. Arrays whose distributions are very different from the others should be considered for possible problems.

Figure 5 shows the density distributions of the log₂ ratios grouped by the mapping of the probes. Blue, density estimate of log₂ ratios of probes annotated "TRUE" in the "hasTarget" slot. Gray, probes annotated "FALSE" in the "hasTarget" slot.

Figure 6 shows a false color heatmap of between arrays distances, computed as the MAD of the M-value for each pair of arrays.
d_ij = c &bull median|x_mi-x_mj|

Here, x_mi is the intensity value of the m-th probe on the i-th array, on the original data scale.
c = 1.4826 is a constant factor that ensures consistency with the empirical variance for Normally distributed data (see manual page of the mad function in R). This plot can serve to detect outlier arrays.
Consider the following decomposition of x_mi: x_mi = z_m + &beta_mi + &epsilon_mi, where z_m is the probe effect for probe m (the same across all arrays), &epsilon_mi are i.i.d. random variables with mean zero and &beta_mi is such that for any array i, the majority of values &beta_mi are negligibly small (i. e. close to zero). &beta_mi represents differential expression effects. In this model, all values d_ij are (in expectation) the same, namely 2 times the standard deviation of &epsilon_mi . Arrays whose distance matrix entries are way different give cause for suspicion. The dendrogram on this plot also can serve to check if, without any probe filtering, the experiments cluster accordingly to a biological meaning.

For each feature, this plot on Figure 7 shows the empirical standard deviation on the y-axis versus the rank of the mean on the x-axis. The red dots, connected by lines, show the running median of the standard deviation. It should be approximately horizontal, that is, show no substantial trend.