4 qscore class

As we point out in the introduction of this document, evaluating the quality of an array-CGH after normalization is of major importance, since it helps answering the following questions:
which is the best normalization process ?
which array is of best quality ?
what is the quality of a given array ?

To this purpose we define quality scores (qscores), which attributes and methods are explianed in the two following subsections.

4.1 Attributes

A qscore object qs is a list which contains a function (qs$FUN), a name (qs$name), and optionnally a label (qs$label) and arguments to be passed to qs$FUN (qs$args). In the following example, the quality score evaluates the percentage of spots that have passed the filtering steps of normalization; it provides an evaluation of the array quality for a given normalization process. The function to.qscore is explained in subsection 4.2.

> <- function(arrayCGH, var) {
+     100 * sum(!$arrayValues[[var]]))/dim(arrayCGH$arrayValues)[1]
+ }
> <- "SPOT_PCT"
> <- "Proportion of spots after normalization"
> <- to.qscore(, name =, 
+     args = alist(var = "LogRatioNorm"), label =

4.2 Methods

4.2.1 to.qscore

The function to.qscore is used of the creation of qscore objects, with the specificities described in subsection 4.1.

> args(to.qscore)
function (FUN, name = NULL, args = NULL, label = NULL, dec = 3) 

4.2.2 qscore.arrayCGH

Function qscore.arrayCGH simply computes and returns the value of qscore for arrayCGH:
> args(qscore.arrayCGH)
function (qscore, arrayCGH) 

4.2.3 qscore.summary.arrayCGH

Function qscore.summary.arrayCGH computes all quality scores of a list (using function qscore.arrayCGH), and displays the results in a convenient way.
> args(qscore.summary.arrayCGH)
function (arrayCGH, qscore.list) 

Pierre Neuvial 2007-03-16