#
nCCl4 quality metrics report

## Experiment Names

1 | 251316214319_auto_479-628
2 | 251316214320_auto_478-629
3 | 251316214321_auto_410-592
4 | 251316214329_auto_429-673
5 | 251316214330_auto_457-658
6 | 251316214331_auto_431-588
7 | 251316214332_auto_492-625
8 | 251316214333_auto_487-712
9 | 251316214379_auto_443-617
10 | 251316214380_auto_493-682
11 | 251316214381_auto_497-602
12 | 251316214382_auto_481-674
13 | 251316214384_auto_450-642
14 | 251316214389_auto_456-694
15 | 251316214390_auto_456-718
16 | 251316214391_auto_475-599
17 | 251316214393_auto_460-575
18 | 251316214394_auto_463-521

## Index

Individual array quality MAplot Spatial distribution of features intensites Homogeneity between experiments Boxplots Density plots Array platform quality Gene mapping Between array comparison Variance mean dependency

## Section 1: Individual array quality

MA plots | Figure 1 | MvA plot 1 MvA plot 2 MvA plot 3

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).

Spatial distribution of features intensites | Figure 2 |  | Spatial plots 1 Spatial plots 2 Spatial plots 3 Spatial plots 4 Spatial plots 5 Spatial plots 6

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.

## Section 2: Homogeneity between experiments

Boxplots | Figure 3

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).

Density plots | Figure 4

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

## Section 3: Array platform quality

Gene mapping | Figure 5

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.

## Section 4: Between array comparison

Heatmap representation of the distance between experiments | Figure 6

Figure 6 shows a false color heatmap of between arrays distances, computed as the MAD of the M-value for each pair of arrays.
dij = c &bull median|xmi-xmj|
Here, xmi 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 xmi: xmi = zm + &betami + &epsilonmi, where zm is the probe effect for probe m (the same across all arrays), &epsilonmi are i.i.d. random variables with mean zero and &betami is such that for any array i, the majority of values &betami are negligibly small (i. e. close to zero). &betami represents differential expression effects. In this model, all values dij are (in expectation) the same, namely 2 times the standard deviation of &epsilonmi . 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.

## Section 5: Variance mean dependency

Standard deviation versus rank of the mean | Figure 7

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.