Hi Jim, Thank you for your response. I am trying to separate the mature and premature microRNAs because they have different hybridization characteristics. I thought it would be more 'statistically correct' to normalize them separately since these two populations are different. If I don't intend to compare the premature and mature microRNAs to each other, are there any disadvantages of separating them? except for the obvious reason - that there is no function available for easy use and I am hardly a programmer. So there's no easy way to coerce a data matrix (or a subset of the data) into an ExpressionFeatureSet structure to satisfy the rma function? Maybe I can convert the matrix into an ExpressionSet using Biobase, do you know if there's a way to to convert the ExpressionSet into an ExpressionFeatureSet? Thank you so much for your help! Dana On Thu, Oct 24, 2013 at 9:07 AM, James W. MacDonald <jmacdon@uw.edu> wrote: > Hi Dana, > > On Wednesday, October 23, 2013 12:04:31 AM, Dana Most wrote: > >> Dear All, >> >> How can I transform/coerce a gene expression matrix into an expression >> set? >> >> I'm using affy miRNA 3.0 data and I would like to normalize only a subset >> of >> the samples (i have 4 groups of samples and would like to choose 2) and >> only a >> subset of microRNAs (I have mature and premature microRNAs and they >> should not >> be normalized together). >> >> It should look something like this: >> affyExpressionFS <- read.celfiles(celFiles, pkgname="pd.mirna.3.0") >> data = exprs(affyExpressionFS) >> data = data[1:1000,1:20] >> > > Why do you think the first 1000 rows are useful here? Is this just > supposed to be an example? > > >> exprsData = coerce data into expression set >> >> rma(exprsData) >> > > You can't run rma on an ExpressionSet, as an ExpressionSet is intended to > contain summarized data. Instead you need to use an ExpressionFeatureSet > object (which is what you are getting your matrix of data out of). > > That said, you will have to do some serious coding if you want to > accomplish this. Right now there is no easy way (that I know of - Benilton > might correct me here) to subset to a particular set of probes. You can > check out the oligo source from subversion and make whatever changes you > want. There is even a 'subset' argument that is for future use that you > could implement if you want. > > But this leads me to your original rationale for wanting to do this, where > you state that mature and precursor miRNAs should not be normalized > together. I am not sure why you would think this, and I am pretty sure you > are wrong. > > You could argue that the hairpin miRNAs are fundamentally different from > the mature miRNAs (which I suppose they are), but that has nothing to do > with normalization. For the normalization to be reasonable, you have to > fulfill two criteria. First, most probes should not be differentially > expressed between samples, and second, the underlying distributions of the > data should not be completely dissimilar. > > This has nothing to do with what the probes are supposed to measure, nor > whether or not the probes are even measuring anything at all. So I don't > see any real reason to separate the hairpin from mature miRNAs prior to > normalizing. > > Best, > > Jim > > > > > >> >> Also, I would like to use array quality metrics package on exprsData >> >> arrayQualityMetrics(**expressionset = exprsData, >> outdir = "exprsData",force = FALSE, do.logtransform = FALSE) >> >> Thank you, >> Dana >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.e="" thz.ch="" mailman="" listinfo="" bioconductor=""> >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor<http: news.gmane.org="" gmane="" .science.biology.informatics.conductor=""> >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > [[alternative HTML version deleted]]