[BioC] Design and normalization of focused arrays
klemens.vierlinger at arcs.ac.at
Fri Sep 2 15:02:01 CEST 2005
Our approach seems to be quite similar to yours, we have small arrays (100-600 probes) of 65mer oligos. This is what we do:
We have Amershams Scorecard controls on our arrays (http://www5.amershambiosciences.com/aptrix/upp01077.nsf/Content/Products?OpenDocument&parentid=63004286&moduleid=165076). This saves us the laborious process of cloning and ivt of bacterial genes. However, the probes included in the set are cDNAs, so you will need to get 70mer probes synthesised. Amersham has licensed this out to a company called tib-molbiol (www.tib-molbiol.de/). Unfortunately their pricing is outrageous, but you cant buy them anywhere else (at least not as far as I know, please let me know if you find them somewhere cheaper). They seem to work well, in an experiment where we hybridised pbmc's against a tumor cell line we found more of the expected genes differentially expressed when normalising via those controls compared to normalising via housekeeping genes. When we do a self-self hybridisation, the controls behave much like the other spots on the array. This, to me, seems a good indication that they do what they are supposed to do.
However, via a QC experiment we found out that the control sequnces are rather AT-rich. All the other probes on the array are designed to be ~50%GC (which I guess is what most people do), and I dont particularly like the idea that the control sequences differ from the rest of the array. This doesnt necessarily have to mean anything, but I am thinking of adding another control kit, stratagenes spotreport (http://www.stratagene.com/products/showCategory.aspx?catId=17) as a control for the controls (am I paranoid or what :)). At the end of the day there is no way you can find out where your baseline really is. All you can do is try to make the least false assumtions!
For normalisation I use the limma functions and the wheights as you describe it.
all the best
Date: Thu, 1 Sep 2005 12:15:22 +0300
From: "Vered Caspi" <veredcc at bgumail.bgu.ac.il>
Subject: [BioC] Design and normalization of focused arrays
To: <bioconductor at stat.math.ethz.ch>
Message-ID: <001301c5aed5$ae54d770$32594884 at veredcc>
I am currently designing a focused human long-oligo (70 mer) array of 150 genes, and I am wondering which control spots should be added to the array to assist normalization, how many spots will be enough, and then how to do the normalization.
Here is some information I already gathered:
In a paper by van de peppel et al from 2003 (http://www.nature.com/cgi-taf/DynaPage.taf?file=/embor/journal/v4/n4/full/embor798.html) the authors used 9 bacterial control RNAs of different concentrations, and printed at least 2 replicates on each array subgrid. Do you think 9 concentrations are enough?
In the Limma UserGuide p.15 it is recommended, for focused arrays "to include on the arrays a series of non-differentially expressed control spots, such as a titration series of whole-library-pool spots, and to use the up-weighting method". I don't understand what is the meaning of "a titration series of whole-library-pool spots", and will appreciate any further details or references on its preparation so I can deliver it to the experimentalists. Also, will it be OK, in Limma, to give positive weight to the control spots only, and weights of 0 to the 150 genes of study?
I will appreciate any further ideas on which controls to include, how many would be enough, and references, if available. Frankly, I am rather a beginner with spotted arrays and with R, but so far used Limma successfully for several spotted array analyses. Therefore, your advice on normalization of these arrays will also be highly appreciated.
With best regards,
Vered Caspi, Ph.D.
Bioinformatics Support Unit, Head
National Institute for Biotechnology in the Negev
Building 39, room 214
Ben-Gurion University of the Negev
Beer-Sheva 84105, Israel
Tel: 08-6479034 054-7915969
More information about the Bioconductor