the study design is case-only
the goal appears to be subtype discovery (or perhaps the clustering was
just a QC step for batch effect detection)
limma might also work, though the distribution of the variance in
methylation data is far from uniform even after transformation.
the experimenter might prefer to use ComBat because it tends to work well
when there is a clear batch effect and a smallish sample size.

question for the experimenter -- did you normalize and background correct
your samples?  Because if not you're going to be dealing with both dye bias
and background effects which tend to differ across samples.

there is a third option for "doubly unsupervised" normalization but I will
leave it up to the author of the method to describe it if he wishes.


On Fri, Jun 8, 2012 at 10:58 AM, Brent Pedersen <bpederse@gmail.com> wrote:

> On Fri, Jun 8, 2012 at 8:44 AM, Femke [guest] <guest@bioconductor.org>
> wrote:
> >
> > Dear All,
> >
> > I have Infinium 450K data for 56 breast cancer tumors. As a first
> analysis I wanted to do a clustering and see the distribution of the
> samples. For this I used the minfi package. Unfortunately, the assays were
> done in 2 batches and there is a clear batch effect. I looked into Combat
> and SVA to remove the batch effect. As far as I understand, to use these
> approaches I need to have a phenotype/variable of interest. In the tutorial
> ("The SVA package for removing batch effects and other unwanted variation
> in high-throughput experiments … Modified: October 24, 2011 Compiled:
> April 25, 2012") the variable of interest is cancer status. However, I do
> not have normals. Does anyone have suggestions on how I should tackle these
> batch effects?
> >
> > Many thanks in advance and all the best!
> >
> > Femke
> >
> >
> >  -- output of sessionInfo():
> >
> > R version 2.15.0 (2012-03-30)
> > Platform: x86_64-pc-linux-gnu (64-bit)
> >
> > locale:
> > [1] C
> >
> > attached base packages:
> > [1] grid      stats     graphics  grDevices utils     datasets  methods
> > [8] base
> >
> > other attached packages:
> >  [1] bladderbatch_1.0.3
> >  [2] sva_3.2.1
> >  [3] mgcv_1.7-17
> >  [4] corpcor_1.6.3
> >  [5] IlluminaHumanMethylation450kmanifest_0.2.1
> >  [6] gplots_2.10.1
> >  [7] KernSmooth_2.23-7
> >  [8] caTools_1.13
> >  [9] bitops_1.0-4.1
> > [10] gdata_2.8.2
> > [11] gtools_2.6.2
> > [12] minfi_1.2.0
> > [13] GenomicRanges_1.8.6
> > [14] IRanges_1.14.3
> > [15] reshape_0.8.4
> > [16] plyr_1.7.1
> > [17] lattice_0.20-6
> > [18] Biobase_2.16.0
> > [19] BiocGenerics_0.2.0
> >
> > loaded via a namespace (and not attached):
> >  [1] AnnotationDbi_1.18.1  BiocInstaller_1.4.4   Biostrings_2.24.1
> >  [4] DBI_0.2-5             MASS_7.3-18           Matrix_1.0-6
> >  [7] R.methodsS3_1.2.2     RColorBrewer_1.0-5    RSQLite_0.11.1
> > [10] affyio_1.24.0         annotate_1.34.0       beanplot_1.1
> > [13] bit_1.1-8             codetools_0.2-8       crlmm_1.14.0
> > [16] ellipse_0.3-7         ff_2.2-7              foreach_1.4.0
> > [19] genefilter_1.38.0     iterators_1.0.6       limma_3.12.0
> > [22] matrixStats_0.5.0     mclust_3.4.11         multtest_2.12.0
> > [25] mvtnorm_0.9-9992      nlme_3.1-104          nor1mix_1.1-3
> > [28] oligoClasses_1.18.0   preprocessCore_1.18.0 siggenes_1.30.0
> > [31] splines_2.15.0        stats4_2.15.0         survival_2.36-14
> > [34] xtable_1.7-0          zlibbioc_1.2.0
> >
> >
> > --
> > Sent via the guest posting facility at bioconductor.org.
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
> Since the batch is known,  why not just include it in your model and
> run with limma or lm()?
> But what's your study-design if you don't have controls?
>
> _______________________________________________
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>



-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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