Hi James,

Thanks for the response. I am a bit new to this kind of analysis. When I
use and  dmpFinder on three different groups Vs Control and I export the
results as data.frame, I only get columns corresponding to probeid,
intercept and F, p and q values but not the group name. I want to have data
in the form how the plotCpG function works (to see a difference in
methylation across the three groups). How can I achieve this?

My initial goal:
Find significantly different probes across three different grades of cancer
samples.
Map the probes to gene regions (like promotes, gene body, UTR, etc)
Find enriched regions.

Any help for the above tasks is appreciated.


Thanks
Srikanth





On Wed, Jun 19, 2013 at 7:06 PM, James W. MacDonald <jmacdon@uw.edu> wrote:

> 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] IlluminaHumanMethylation450kma**nifest_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
>
>


-- 
Srinivas Srikanth Manda
Ph.D. Student
Institute of Bioinformatics
Discoverer, 7th Floor,
International Technology Park,
Bangalore, India
Mob:+919019114878

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