[BioC] saturated spots

[J.delasHeras at ed.ac.uk]

Quoting Hans-Ulrich Klein <h.klein at uni-muenster.de>:

> Dear all,
>
> I currently analyse some selfmade oligo chips. The green channel
> contains the results of a ChIP-experiment. The red one contains genomic
> DNA. I read in the data and did some quality plots using limma.
> Unfortunatly, most arrays show strong saturation effects.
>
> Some plots for interested readers:
> MA-plot: http://img402.imageshack.us/img402/6323/maplotqa1.png
> density: http://img146.imageshack.us/img146/482/densitynotlogjo9.png
> density log2: http://img183.imageshack.us/img183/9416/densitylogmt8.png
>
> It is certainly not a good idea to ignore the saturation. The saturated
> spots are not flagged by the image analysis software. (Only non-flagged
> spots are plotted in the images above.) My solution is to set a
> threshold value for each array manually just before the saturation peak
> (using the density plots) and then flag all spots with intensities
> larger than the threshold. The flagged spots are not used for
> normalization and further analysis.
>
> Are there any R-packages dealing with saturation problems? Maybe for
> detecting the threshold automatically or for correcting saturated spots
> with non-linear transformations. I have found none.
>
> How do you handle such saturation effects in your data?
>
> Thank you very much for any suggestions,
> Hans-Ulrich

Hi Hans,

there are clearly some saturated spots, but I am not sure you need to  
worry too much. They're still a small proportion of the total. I  
suppose it is reasonable to flagged the saturated spots (which you can  
find by looking at the actual raw intensity) and not include those in  
the normalisation. However I would not necessarily remove them from  
the subsequent analysis. If a spot is saturated for one channel, but  
is weak enough in the other, it may be still interesting despite the  
fact that the ratio will be off: it'll still be "big enough". The  
ratios you obtain from your arrays will not be anywhere as accurate as  
what you'll get when you validate results by PCR. They give you an  
idea of what's going on, but if you want real quantitation you have to  
validate those spots by real time (or even semiquantitative) PCR, so I  
wouldn't worry too much about some degree of saturation. saturated  
spots are still informative (bright!), depending on what happens on  
the other channel.

You can use the flags to add a note of "attention" when you deal with  
those spots, if they appear in your final list of interesting genes,  
and decide individually which ones you want to trust.

Perhaps you should look also into the actual sequences that give you  
the brightest signals (saturated). Perhaps you find out that the  
reason they're so bright is they're present in multiple copies in the  
genome (even if you filter them out during design, some may creep  
in)... in which case you could just ignore them from the beginning.

As for correcting the range to account for saturated spots... I think  
the aroma package allows you to deal with multiple scans of the same  
slide, using different PMT settings, to re-arrange the dynamic range.  
This seems especially useful when you have some very bright spots you  
don't want to lose, but which will saturate if you scane to get decent  
intensities on the much weaker bulk. I haven't tried it myself. I  
tried another approach (MASLINER) and it did seem to work, but in teh  
end I decided it wasn't worth the effort in my particular case.

Jose

-- 
Dr. Jose I. de las Heras                      Email: J.delasHeras at ed.ac.uk
The Wellcome Trust Centre for Cell Biology    Phone: +44 (0)131 6513374
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