[BioC] Need help: no MTC possible
James W. MacDonald
jmacdon at uw.edu
Tue Oct 16 15:48:13 CEST 2012
Hi Suparna,
On 10/16/2012 5:58 AM, suparna mitra wrote:
> Hello group,
> Related to my previous post, I further tried arrayweight as:
>
> > f.invivo <- factor(InVivoTargets$Treatment, levels = c("A", "R", "T"))
>
> > design.invivo <- model.matrix(~0 + f.invivo)
>
> > colnames(design.invivo) <- c("A", "R", "T")
>
> > design.invivo
>
> A R T
>
> 1 1 0 0
>
> 2 1 0 0
>
> 3 1 0 0
>
> 4 1 0 0
>
> 5 1 0 0
>
> 6 1 0 0
>
> 7 0 1 0
>
> 8 0 1 0
>
> 9 0 1 0
>
> 10 0 1 0
>
> 11 0 1 0
>
> 12 0 1 0
>
> 13 0 0 1
>
> 14 0 0 1
>
> 15 0 0 1
>
> 16 0 0 1
>
> 17 0 0 1
>
> 18 0 0 1
>
> attr(,"assign")
>
> [1] 1 1 1
>
> attr(,"contrasts")
>
> attr(,"contrasts")$f.invivo
>
> [1] "contr.treatment"
>
>
> >
>
> > arrayw <- arrayWeightsSimple(rmaOligoinvivo, design.invivo)
>
> > fit <- lmFit(rmaOligoinvivo, design.invivo, weights=arrayw)
>
> > arrayw
>
> 1 2 3 4 5 6 7
> 8 9 10 11 12 13 14
>
> 0.3749711 0.8578285 1.9289731 1.2390065 0.8116796 1.7846502 1.0741852
> 1.4277605 0.6533368 0.7637412 1.2647738 1.4520790 0.8309346 0.9328655
>
> 15 16 17 18
>
> 1.1926458 0.7280477 0.5130294 1.8503073
>
> > contrast.matrix.invivo <- makeContrasts(R-A, T-R, T-A,levels =
> design.invivo)
>
> > fit2<-contrasts.fit(fit, contrast.matrix.invivo)
>
> > fit2 <- eBayes(fit2)
>
Looks good to me.
Best,
Jim
> >
>
> Can anybody please suggest if I am doing it right? Actually being new
> in this I am bit afraid to make errors.
> Thanks,
> Suparna.
>
> On 16 October 2012 10:36, suparna mitra <smitra at liverpool.ac.uk
> <mailto:smitra at liverpool.ac.uk>> wrote:
>
> 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 at uw.edu
> <mailto:jmacdon at 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]]
>
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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
>
>
>
>
> --
> 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 <tel:%2B44%20%280%29151%20795%205394>,
> Internal ext: 55394
> M: +44 (0) 7511387895 <tel:%2B44%20%280%29%207511387895>
> Email id: smitra at liverpool.ac.uk <mailto:smitra at liverpool.ac.uk>
> Alternative Email id: suparna.mitra.sm at gmail.com
> <mailto:suparna.mitra.sm at gmail.com>
>
>
>
>
> --
> 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 at liverpool.ac.uk <mailto:smitra at liverpool.ac.uk>
> Alternative Email id: suparna.mitra.sm at gmail.com
> <mailto:suparna.mitra.sm at gmail.com>
>
--
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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