> Thanks to Adai and Eric, > > Well, I'm trying to bring back the discussion to the previous direction as > it apparently went to a different area : cross-platform integration. :) > I > was wondering about integration within the same platform ? an issue when > there are multiple chips (in case of affymetrix) OR multiple > print layouts (cDNA .gal files). > > " have to normalize all raw data together. All data should also be of one > platform only. Then you can simply normalize all CEL files or all ----- > files together and be done." [courtesy: Balasubramanian] > > So, if I have suppose MA1, MA2 as respective normalised datasets (same > platform. After doing normalization based on chip-types in case of > Affymetrix, OR, print layouts in case of cDNA), can I just normalize them > again for the final dataset, or I need to take care of some other issues > too > (how to tackle!) ? Also, wonder if there's any smart package in this > regard! I am no certain that there is a magic package that can do the best data transformation for all situations. You may well have to include a dataset effect into your analysis (as Robert and others are recommending it), and there are many (smart) packages available in R to help you build models and estimate effects. > > Also Eric, I didn't get you what project you were talking about : " See > for > example the oncomine project ." The project aims at bundling heterogeneous expression data together. http://www.ncbi.nlm.nih.gov/pubmed/15068665?dopt=AbstractPlus > Thanks. > > Kathy > > > > > = = = = = = = = > > On Thu, Apr 24, 2008 at 7:34 AM, Kort, Eric <eric.kort at="" vai.org=""> wrote: > >> > -----Original Message----- >> > From: bioconductor-bounces at stat.math.ethz.ch >> > Subject: Re: [BioC] Merging microarray datasets >> > >> > >> > This is an interesting question and one that I like to >> > explore further. >> > >> > The papers I have seen on combining microarray datasets so >> > far select one algorithm for Affymetrix and one algorithm for cDNA. >> > >> > Has anyone investigated which combination of preprocessing >> > algorithm(s) make data from these two platforms comparable? >> > Indeed, how does one check if they are comparable? Any >> > references and suggestions would be very welcome. >> >> Platforms aside, cDNA arrays are usually two color and ratios, and Affy >> are one color and not ratios. >> >> So one approach is to turn everything into ratios after preprocessing >> and >> normalization (using, for example, a suitable set of reference >> samples...e.g. normal kidney for kidney tumors). Obviously, thoughtful >> selection of reference samples is required, and the reference chosen >> should >> be analagous to whatever was used as the reference in the two color >> arrays. >> >> Then, one can try to bring things further into line by converting the >> log >> transformed ratios into z scores. >> >> As far as verification, a place to start would be box and whisker plots >> to >> expose obvious abnormalities. You can also perform unsupervised >> clustering >> to see if the samples cluster mainly according to platform/lab or mainly >> according to known phenotype. >> >> Then, as Robert Gentleman stated, you can use appropriate models to >> correct systematic biases. But I will leave the details of that to the >> statisticians (and, indeed, the archives of this list). >> >> Obviously, the whole exercise is frought with difficulties, but it is >> done. See for example the oncomine project. Whether it is is fruitfully >> done is open to argument. One thing to consider is to utilize >> down-stream >> analysis methods that care more about relative position of genes and >> less >> about magnitude of values (e.g. GSEA or PGSEA). >> >> > >> > Thank you. >> > >> > Regards, Adai >> > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor

Kathy, this depends on two thing. 1) how similar are the chip types? For example, hgu95a and hgu95av2, two Affymetrix chips which differed by one probeset. I do not know why it differed by one probeset but I suppose one just omitted the extra probeset and preprocess them together. 2) the type of preprocessing algorithm used (and sample size) If you are using preprocessing algorithms that work on array by array basis (e.g. median scaling), then you can normalize the different chip types differently followed by a merge(). Creating missing values for the probes not present in one type of chip but in others. Next, you can either try to adjust the expression values for possible biases (e.g. see Benito PMID:14693816) or include a chip type indicator in your analysis as Gentlemen and others have pointed out. If you are using algorithms that take information across chips (e.g. RMA) AND you only have small number of arrays for each chip type, then you need to give more thought. It would be worth exploring if one can merging the chips at probe-level (e.g. matchprobes package) to benefit from better parameter estimation. Regards, Adai Kathy, sorry for bring up the issue of preprocessing. It is relevant and I will raise in a separate thread. Kathy Duncan wrote: > Thanks to Adai and Eric, > > Well, I'm trying to bring back the discussion to the previous direction as > it apparently went to a different area : cross-platform integration. :) I > was wondering about integration within the same platform ? an issue when > there are multiple chips (in case of affymetrix) OR multiple > print layouts (cDNA .gal files). > > "? have to normalize all raw data together. All data should also be of one > platform only. Then you can simply normalize all CEL files or all ----- > files together and be done." [courtesy: Balasubramanian] > > So, if I have suppose MA1, MA2? as respective normalised datasets (same > platform. After doing normalization based on chip-types in case of > Affymetrix, OR, print layouts in case of cDNA), can I just normalize them > again for the final dataset, or I need to take care of some other issues too > (how to tackle!) ? Also, wonder if there's any smart package in this regard! > > > Also Eric, I didn't get you what project you were talking about : "?See for > example the oncomine project?." > > Thanks. > > Kathy >