Dear James,
  Thanks for your suggestion. I was reading arrayWeights package in limma.
But being novice in bioC I have one confusion. Should I
perform arrayWeights on normalized (rmaOligo) expression data or on the raw
data?

This is what i have done so far:

> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] statmod_1.4.15              limma_3.12.1
 annotate_1.34.1             hugene10stprobeset.db_8.0.1 org.Hs.eg.db_2.7.1

 [6] BiocInstaller_1.4.7         affycoretools_1.28.0        KEGG.db_2.7.1
              GO.db_2.7.1                 AnnotationDbi_1.18.1
[11] affy_1.34.0                 Biobase_2.16.0
 BiocGenerics_0.2.0          pd.hugene.1.0.st.v1_3.6.0   RSQLite_0.11.1

[16] DBI_0.2-5                   oligo_1.20.4
 oligoClasses_1.18.0


 rmaOligoinvivo = oligo::rma(InVivodat1)
Background correcting
Normalizing
Calculating Expression

> maplot(rmaOligoinvivo)
>hist(rmaOligoinvivo)
> InVivoTargets=readTargets("~/Desktop/Recent/Liverpool-work-
related/Micro_RawData/InVivoTargets.txt")
> InVivoTargets
 FileName Treatment
1   MC1       A
2   MC2       A
3   MC3       A
4   MC4       A
5   MC5       A
6   MC6       A
7   MC7       R
8   MC8        R
9   MC9        R
10 MC10        R
11 MC11        R
12 MC12        R
13 MC13       T
14 MC14        T
15 MC15        T
16 MC16        T
17 MC17        T
18 MC18        T

f.invivo <- factor(InVivoTargets$Treatment, levels = c("A", "R", "T"))

design.invivo <- model.matrix(~0 + f.invivo)

> colnames(design.invivo) <- c("A", "R", "T")

> fit.invivo <- lmFit(rmaOligoinvivo, design.invivo)

> contrast.matrix.invivo <- makeContrasts(R-A, T-R, T-A,levels =
design.invivo)

> fit2.invivo <- contrasts.fit(fit.invivo, contrast.matrix.invivo)

> fit2.invivo <-eBayes(fit2.invivo)

Thanks a lot,
Suparna.


On 15 October 2012 14:33, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi Suparna,
>
>
> On 10/15/2012 7:01 AM, suparna mitra wrote:
>
>> Hi all,
>>    I have been working in a project where I have Affymetrix Hgene 1.0 St
>> V1
>> data. And I have tree groups of patients having 6 samples each. I tried to
>> perform rma normalization and to filter my data based on expression values
>> 20%. After that went for unpaired t-test to test each two combination of
>> groups. But the problem is my data is extremely variable.
>> I have tried to filter my genes based on variance and/or CV before
>> testing,
>> to try to reduce the number of genes entering your test and multiple
>> correction.  But with different reasonable filtering also I am with no
>> luck. And I don't have the option to increase sample size of my project.
>> Further I tried to check for the bad samples and bad probes from
>> experimentand remove outlier if these are not of interest. Still the same
>> when run t-test (and other possible test like Mann-Whitney) with MTC there
>> are no genes.
>> On the other hand if I go on with out MTC and select a good p value cutoff
>> and reasonable fold change I get a list of significant gene which may be
>> good or reasonable for my study. but the problem is I somehow need to
>> justify the method for my finding. Do you know any study or paper where
>> anybody has treated their data without MTC?
>> My main concern is if I find a good story matching biological prospective,
>> would it be anyhow possible to justify the method without MTC?
>>
>
> It's not clear to me what you are doing here - when you filter on variance
> are you keeping or removing the high variability genes (keeping, I hope)? I
> am also not sure what MTC stands for - is this multiple test correction?
>
> Anyway, assuming I have things correct, some suggestions. First, you might
> want to use array weights when fitting your model. If you have a lot of
> intra-group variability, this will tend to help.
>
> Second, the t-statistic is the universally most powerful test (assuming
> the underlying data are relatively hump-shaped), so going to a
> non-parametric test will usually reduce rather than increase power to
> detect differences.
>
> Third, univariate tests are arguably not the most sophisticated way of
> analyzing expression data, and you might get better (or at least more
> satisfactory) results if you instead looked at analyzing for groups of
> genes rather than individually.
>
> Depending on your experiment, you could accomplish this task with a gene
> set analysis (there are multiple ways of doing this - perhaps the easiest
> being romer() and roast() in limma), or if you have phenotypic data,
> especially continuous measures, a WGCNA analysis might be of some use.
>
> Best,
>
> Jim
>
>
>  Thanks a lot,
>> Suparna.
>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________**_________________
>> Bioconductor mailing list
>> Bioconductor@r-project.org
>> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https://stat.ethz.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
>
>


-- 
Dr. Suparna Mitra
Wolfson Centre for Personalised Medicine
Department of Molecular and Clinical Pharmacology
Institute of Translational Medicine University of Liverpool
Block A: Waterhouse Buildings,  L69 3GL Liverpool

Tel.  +44 (0)151 795 5394, Internal ext: 55394
M: +44 (0) 7511387895
Email id: smitra@liverpool.ac.uk
Alternative Email id: suparna.mitra.sm@gmail.com

	[[alternative HTML version deleted]]

