6 Graphical representations

As for any type of data analysis, appropriate graphical representations are of major importance for data understanding. Array-CGH data are typically ratios or log-ratios, that correspond to locations on the array (spots) and to locations on the genome (clones). Therefore in the case of array-CGH data normalization, two complementary types of representations are necessary:

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a dotplot of the array, that takes into account the array design. This is a crucial tool in the case of array-CGH data normalization for two reasons: first it provides an easy way to identify spatial artifacts such as row, column, print-tip group effects, as well as spatial bias and spatial gradients on the array; then it allows a post-normalization control, to ensure that the normalization procedure reached its goals, i.e. significantly reduced the observed effects.

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a plot of the signal values along the genome, which gives a visual impression of the array quality on the edge of biological relevance; comparing the signal shape before and after normalization provides a qualitative idea of the imrpovement in data quality provided by the normalization method.

The arrayPlot method provided by the GLAD package and based on maImage (2) addresses the first point; we add two methods to this toolbox:

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the genome.plot method displays a plot of any signal value (e.g. log-ratios) along the genome;

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the report.plot method successively calls arrayPlot and genome.plot in order to provide a simultaneous vision of the data using the two relevant metrics (array and genome), with approproate color scales.

6.1 genome.plot

This method provides a convenient way to plot a given signal along the genome; the signal values can be colored according to their level (which is the default comportment of the function) or to the level of any other variable, in the following way:

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if the variable is numeric (e.g. signal to noise ratio), the function assumes that it is a quantitative variable and adapts a color palette to its values (figure 3)

Figure 3: Pan-genomic profile of the array. Colors are proportional to log-ratio values.

> data(spatial)
> genome.plot(edge.norm, chrLim = "LimitChr")

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if the variable is not numeric (e.g. the copy number variation as estimated by GLAD, or a character variable making the disitnction between flagged and un-flagged clones), the function counts the number of modalities of the variable and defines an appropriate color scale using the rainbow function (figure 4).

Figure 4: Pan-genomic profile of the array. Colors correspond to the values of the variable ``ZoneGNL''.

> data(spatial)
> edge.norm$cloneValues$ZoneGNL <- as.factor(edge.norm$cloneValues$ZoneGNL)
> genome.plot(edge.norm, col.var = "ZoneGNL", chrLim = "LimitChr")

6.2 report.plot

This method successively calls arrayPlot and genome.plot; it checks for color scale consistency between plots, and can automatically set the plot layout (figure 5).

Figure 5: report.plot: array image and pan-genomic profile after normalization.

> data(spatial)
> report.plot(edge.norm, chrLim = "LimitChr", zlim = c(-1, 1))