[BioC] Help with DMPFinder in minfi package

[James W. MacDonald]

Hi Srinivas,

On 6/19/2013 5:21 AM, Srinivas Srikanth Manda wrote:
> Hello Members,
>
> I am using Minfi package to analyze 450k data. I have three different
> groups of samples and one common control. I did the normalization and other
> steps according to manual, but stuck at the differential methylation
> positions. When I use:
>
> M<- getM(MSet.norm, type = "beta", betaThreshold = 0.001)
> dmp1<- dmpFinder(M, pheno=pd$Sample_Group, type="categorical")
>
> I want to get a table with probes and corresponding values in each group.
> the data.frame dmp1 does not tell me which group has what value? How can I
> do that?

It's not clear what you mean by 'probes and corresponding values in each 
group'. I am not sure what a corresponding value is.

If I make the assumption that you want the coefficients from the model 
fit, then you can do

design <- model.matrix(~pd$Sample_Group)
fit <- lmFit(M, design)

and then fit$coefficients has the coefficients. Or perhaps you just want 
the methylation values? The M-values are in your M matrix, and if you 
prefer betas, you can use getBeta(MSet.norm).

You might also just want the mean of each group. In which case it would 
be easier to do

design <- model.matrix(~0+pd$Sample_Group)
fit <- lmFit(M, design)

and then fit$coefficients will contain the mean value for each group, by 
probe.

Best,

Jim


>
>
> sessionInfo()
> R version 2.15.2 (2012-10-26)
> Platform: x86_64-unknown-linux-gnu (64-bit)
>
> locale:
>   [1] LC_CTYPE=en_US.utf8       LC_NUMERIC=C
>   [3] LC_TIME=en_US.utf8        LC_COLLATE=en_US.utf8
>   [5] LC_MONETARY=en_US.utf8    LC_MESSAGES=en_US.utf8
>   [7] LC_PAPER=C                LC_NAME=C
>   [9] LC_ADDRESS=C              LC_TELEPHONE=C
> [11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
>   [1] minfiData_0.3.1
>   [2] IlluminaHumanMethylation450kmanifest_0.4.0
>   [3] minfi_1.4.0
>   [4] Biostrings_2.26.3
>   [5] GenomicRanges_1.10.7
>   [6] IRanges_1.16.6
>   [7] reshape_0.8.4
>   [8] plyr_1.8
>   [9] lattice_0.20-15
> [10] Biobase_2.18.0
> [11] BiocGenerics_0.4.0
>
> loaded via a namespace (and not attached):
>   [1] affyio_1.26.0         annotate_1.36.0       AnnotationDbi_1.20.7
>   [4] beanplot_1.1          BiocInstaller_1.8.3   bit_1.1-10
>   [7] codetools_0.2-8       crlmm_1.16.9          DBI_0.2-7
> [10] ellipse_0.3-8         ff_2.2-11             foreach_1.4.0
> [13] genefilter_1.40.0     grid_2.15.2           iterators_1.0.6
> [16] limma_3.14.4          MASS_7.3-23           Matrix_1.0-12
> [19] matrixStats_0.8.1     mclust_4.1            multtest_2.14.0
> [22] mvtnorm_0.9-9994      nor1mix_1.1-4         oligoClasses_1.20.0
> [25] parallel_2.15.2       preprocessCore_1.20.0 RColorBrewer_1.0-5
> [28] RcppEigen_0.3.1.2.1   R.methodsS3_1.4.2     RSQLite_0.11.3
> [31] siggenes_1.32.0       splines_2.15.2        stats4_2.15.2
> [34] survival_2.37-4       tools_2.15.2          XML_3.96-1.1
> [37] xtable_1.7-1          zlibbioc_1.4.0
>
>
>
> Regards,
> Srikanth
>
>

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