Nimblegen chip data analysis
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Andrew Kern ▴ 10
@andrew-kern-894
Last seen 10.3 years ago
has anyone out there used bioconductor to analyze nimblegen oligonucleotide chip data? if so could you point me in the right direction to get me started? i've never before analyzed microarray data and i'm hoping to use bioconductor for this project. cheers, andrew kern
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@sean-davis-490
Last seen 4 months ago
United States
Andrew, Nimblegen arrays are simply two-color arrays on (legal) steroids. You can simply treat them as such. One thing to be careful of, though, is what you assume about differential expression if you have a highly customized array. There aren't methods to read Nimblegen data, so you will probably have to read it with standard R functions (read.table, for example). Decide what hypotheses make sense for your data and think about what makes sense for normalization (again, depends on the chip design, to some degree). Sean On 8/27/04 17:51, "Andrew Kern" <adkern@ucdavis.edu> wrote: > has anyone out there used bioconductor to analyze nimblegen > oligonucleotide chip data? if so could you point me in the right > direction to get me started? i've never before analyzed microarray data > and i'm hoping to use bioconductor for this project. > cheers, > andrew kern > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >
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Sean Davis wrote: > Andrew, > > Nimblegen arrays are simply two-color arrays on (legal) steroids. You can > simply treat them as such. Sorry, I feel like I should jump in and correct this. Most NimbleGen arrays are one-color arrays, similar to Affymetrix arrays. We do some two-color arrays, but most of it is not for expression studies. > One thing to be careful of, though, is what you assume about > differential expression if you have a highly customized array. There > aren't methods to read Nimblegen data, so you will probably have to > read it with standard R functions (read.table, for example). Decide > what hypotheses make sense for your data and think about what makes > sense for normalization (again, depends on the chip design, to some > degree). Due to the high level of customization for our array designs, we haven't invested the time to develop a specialized set of tools to load NimbleGen data into Bioconductor. I read in data with read.table and pull out row/columns as needed. Most of the Bioconductor packages have lower level functions that allow you to pass a matrix or dataframe as an argument, instead of the typical AffyBatch or expression set. It's a little more work, and sometimes a bit painful, but it does allow for lots of flexibility. Sean's point about normalization is a good one - depending on the application, you'll find that some methods of normalization are more appropriate than others. It might take a bit of experimentation to figure out which normalization method is best suited to your design/samples/application. Regards, Todd > > Sean > > On 8/27/04 17:51, "Andrew Kern" <adkern@ucdavis.edu> wrote: > > >>has anyone out there used bioconductor to analyze nimblegen >>oligonucleotide chip data? if so could you point me in the right >>direction to get me started? i've never before analyzed microarray data >>and i'm hoping to use bioconductor for this project. >>cheers, >>andrew kern >> -- ****************************************** Todd Richmond, PhD Manager of Research Informatics NimbleGen Systems, Inc. Email: todd@nimblegen.com Phone: 1-608-218-7651
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@sean-davis-490
Last seen 4 months ago
United States
Andrew, Nimblegen is just two-color data, but on a grand scale. I would look at limma or marray packages, but if you have a custom array for a specialized purpose, other packages may be more appropriate. You will have to read the data yourself, so you will probably have to learn to use read.table (see 'Introduction to R manual' and 'R data import/export', both on the CRAN site). (There is not a package for reading Nimblegen data.) Sean ----- Original Message ----- From: "Andrew Kern" <adkern@ucdavis.edu> To: <bioconductor@stat.math.ethz.ch> Sent: Friday, August 27, 2004 5:51 PM Subject: [BioC] Nimblegen chip data analysis > has anyone out there used bioconductor to analyze nimblegen > oligonucleotide chip data? if so could you point me in the right > direction to get me started? i've never before analyzed microarray data > and i'm hoping to use bioconductor for this project. > cheers, > andrew kern > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >
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@sean-davis-490
Last seen 4 months ago
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Not sure why this popped up again, but I stand corrected on the two- color point. Nimblegen has probesets similar to affy design, as pointed out in a post earlier today. Sean ----- Original Message ----- From: "Sean Davis" <sdavis2@mail.nih.gov> To: <bioconductor@stat.math.ethz.ch>; "Andrew Kern" <adkern@ucdavis.edu> Sent: Saturday, August 28, 2004 9:03 AM Subject: Re: [BioC] Nimblegen chip data analysis > Andrew, > > Nimblegen is just two-color data, but on a grand scale. I would look at > limma or marray packages, but if you have a custom array for a specialized > purpose, other packages may be more appropriate. You will have to read the > data yourself, so you will probably have to learn to use read.table (see > 'Introduction to R manual' and 'R data import/export', both on the CRAN > site). (There is not a package for reading Nimblegen data.) > > Sean > > ----- Original Message ----- > From: "Andrew Kern" <adkern@ucdavis.edu> > To: <bioconductor@stat.math.ethz.ch> > Sent: Friday, August 27, 2004 5:51 PM > Subject: [BioC] Nimblegen chip data analysis > > > > has anyone out there used bioconductor to analyze nimblegen > > oligonucleotide chip data? if so could you point me in the right > > direction to get me started? i've never before analyzed microarray data > > and i'm hoping to use bioconductor for this project. > > cheers, > > andrew kern > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >
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