[BioC] arrayQualityMetrics, Outlier

[audrey at ebi.ac.uk]

Dear Yisong,

I need to update the vignette to explain all of this.
Here is how the outlier detection is performed:
For the MA-plot, the mean of the absolute value of M is computed for each
array and those that lie beyond the extremes of the boxplot's whiskers are
considered as possible outliers arrays. The same approach, i.e. using the
whiskers of the boxplot, is applied to the following: the mean and
interquartile range (IQR) from the boxplots and NUSE, the sums of the rows
of the distance matrix (for the heatmap), and the amplitude of low
frequencies of the periodogram (for the spatial intensity distribution).
In the case of the RLE plot, any array with a median RLE higher than 0.1
is considered as a possible outlier.

I am not sure I understand what you mean by what is saturation effect and
what is  background intensity distributions. Do you want definition of
what is saturation and background?

Best regards,
Audrey

--
Audrey Kauffmann
EMBL - EBI
Cambridge UK
http://www.ebi.ac.uk/~audrey


> Dear Bioconductor users,
>
> I used arrayQualityMetrics to generate quality report. But, in its
summary
> report, how does it determine array which has potential problem or as
being
> an outlier? I mean, is there some kind of cutoff  for MA, Boxplot and
heatmap? If it is yes, where can I find them?
>
> What is saturation effect? What is  background intensity distributions.
Dr.
> Hunber explained it before on how to interpret the *Standard deviation
versus rank of the mean*, but I still do not understand its biological
meaning.
>
> Thanks in advance.
>
> Yisong
>
> 	[[alternative HTML version deleted]]
>
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