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
>>
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>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>

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