#
nCCl4 quality metrics report

## Summary

Array # | Array Name | MA-plot | Spatial distribution | Boxplots/Density plots | Heatmap
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 |  |  |  |

*array identified as having a potential problem or as being an outlier.

## Index

Individual array quality MAplot Spatial distribution of features intensites Row-Column effect Homogeneity between arrays 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. 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 the 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 arrays

Boxplots | Figure 4

Figure 4 presents boxplots of the log2(Intensities). Each box corresponds to one array. The left panel corresponds to the red channel. The middle panel shows the green channel. The right panel shows the boxplots of log2(ratio). If the arrays are homogeneous, the boxes should have similar wides and y position.

Density plots | Figure 5

Figure 5 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

Probes mapping | Figure 6

Figure 6 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 arrays | Figure 7

Figure 7 shows a false color heatmap of between arrays distances, computed as the median absolute difference of the M-value for each pair of arrays.
dxy = median|Mxi-Myi|
Here, Mxi is the M-value of the i-th probe on the x-th array, without preprocessing.
This plot can serve to detect outlier arrays.
Consider the following decomposition of Mxi: Mxi = zi + βxi + εxi, where zi is the probe effect for probe i (the same across all arrays), εxi are i.i.d. random variables with mean zero and βxi is such that for any array x, the majority of values βxi are negligibly small (i. e. close to zero). βxi represents differential expression effects. In this model, all values dxy are (in expectation) the same, namely 2 times the standard deviation of εxi . 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 arrays cluster accordingly to a biological meaning.

## Section 5: Variance mean dependency

Standard deviation versus rank of the mean | Figure 8

For each feature, the plot on Figure 8 shows the empirical standard deviation of the intensities of all the arrays on the y-axis versus the rank of the mean of intensities of the arrays on the x-axis. The red dots, connected by lines, show the running median of the standard deviation. After vsn normalization, this should be approximately horizontal, that is, show no substantial trend.

This report has been created with arrayQualityMetrics 1.6.1 under R version 2.7.1 (2008-06-23)