Hi Allegra,

I'm having some difficulties in identifying Differentially Methylated
Regions on my datasets too,
both with bumphunter/minfi and with methyAnalysis package.

With regard to bumphunter/minfi there was a clarification also to me
in the previous massages of the BioC list.

With regard to methyAnalysis I think you performed the smoothMethyData
function separately
It isn't wrong; in fact, if you had not smoothing separately, the next
method of the pipeline detectDMR.slideWin
performs it first and then proceeds with differential analysis.

According to me you could try setting a different
p.value.detection.thargument and try to see if you get results.

Instead I get results in identifying Differentially Methylated Regions by
methyAnalysis but
none is significant (p.adjust = 1 or p.adjust > 0.7).

So I'm wondering could the small number of samples (I've 8-12 samples for
now) affect
the significance of results and lead to a p.adjust not so good?

Best,
Giovanni


Laboratory of Preclinical and Translational Research
IRCCS - CROB Oncology Referral Center of Basilicata
Rionero in Vulture - Italy

*** Mail from ************************
Bioconductor Digest, Vol 132, Issue 26
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Date: Mon, 24 Feb 2014 14:52:58 -0800 (PST)
From: "Allegra Petti [guest]" <guest@bioconductor.org>
To: bioconductor@r-project.org, apetti@genome.wustl.edu
Cc: methyAnalysis Maintainer <dupan.mail@gmail.com>
Subject: [BioC] methyAnalysis for DMR identification
Message-ID: <20140224225258.99BC91468A7@mamba.fhcrc.org>


Hello,

I was wondering if anyone here has tried methyAnalysis for identifying
Differentially Methylated Regions in Illumina 450k array data. It seems to
have wonderful data-visualization options, but it found no
differentially-methylated regions in my data (56 450k data sets), which was
surprising. I've found very little documentation for this method, and I am
wondering if I did something wrong - for example, should I have performed
the smoothMethyData function separately, with a different window size?
Relevant code snippets and output are provided below. I'd greatly
appreciate any thoughts or suggestions.

Thank you very much!
Allegra
__________________________________________________________
Code:

library(lumi);
library(methylumi);
library(wateRmelon);
library(methyAnalysis);
library(IlluminaHumanMethylation450k.db);
library(IlluminaHumanMethylation450kanno.ilmn12.hg19); # Note: had to be
installed manually
barcodes <- readLines("barcodes.txt"); # read barcodes from a text file
classes <- readLines("IdatClasses.txt");
data.s <- methylumIDAT(barcodes); # read in idat files (methylumi function)
*** additional data processing ***
data.m <- as(data.s, "MethyLumiM"); # convert to MethyLumiM object
data.g <- MethyLumiM2GenoSet(data.m, lib =
"IlluminaHumanMethylation450k.db"); # convert MethyLumiM to MethyGenoSet
dmrResult <- detectDMR.slideWin(data.g, sampleType=classes); # smooth data
and find DMRs
allDMRInfo <- identifySigDMR(dmrResult);
___________________________________________________________________
Standard Output:

65 probes were removed because of lack of chromosome location information!
Smoothing Chromosome chr1 ...
Smoothing Chromosome chr10 ...
Smoothing Chromosome chr11 ...
Smoothing Chromosome chr12 ...
Smoothing Chromosome chr13 ...
Smoothing Chromosome chr14 ...
Smoothing Chromosome chr15 ...
Smoothing Chromosome chr16 ...
Smoothing Chromosome chr17 ...
Smoothing Chromosome chr18 ...
Smoothing Chromosome chr19 ...
Smoothing Chromosome chr2 ...
Smoothing Chromosome chr20 ...
Smoothing Chromosome chr21 ...
Smoothing Chromosome chr22 ...
Smoothing Chromosome chr3 ...
Smoothing Chromosome chr4 ...
Smoothing Chromosome chr5 ...
Smoothing Chromosome chr6 ...
Smoothing Chromosome chr7 ...
Smoothing Chromosome chr8 ...
Smoothing Chromosome chr9 ...
Smoothing Chromosome chrX ...
Smoothing Chromosome chrY ...
[1] "Done with detectDMR.slideWin\n"
No significant CpG-sites were identified based on current criteria!



 -- output of sessionInfo():

R version 3.0.2 (2013-09-25)
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=C
 [5] LC_MONETARY=en_US.utf8    LC_MESSAGES=en_US.utf8
 [7] LC_PAPER=en_US.utf8       LC_NAME=C
 [9] LC_ADDRESS=C              LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C

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

other attached packages:
 [1] MASS_7.3-29
 [2] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
 [3] minfi_1.9.11
 [4] bumphunter_1.2.0
 [5] locfit_1.5-9.1
 [6] iterators_1.0.6
 [7] foreach_1.4.1
 [8] Biostrings_2.30.1
 [9] lattice_0.20-24
[10] methyAnalysis_1.4.2
[11] GenomicRanges_1.14.4
[12] XVector_0.2.0
[13] IRanges_1.20.6
[14] wateRmelon_1.2.2
[15] ROC_1.38.0
[16] IlluminaHumanMethylation450k.db_2.0.7
[17] org.Hs.eg.db_2.10.1
[18] RSQLite_0.11.4
[19] DBI_0.2-7
[20] AnnotationDbi_1.24.0
[21] limma_3.18.4
[22] methylumi_2.8.0
[23] matrixStats_0.8.14
[24] ggplot2_0.9.3.1
[25] reshape2_1.2.2
[26] scales_0.2.3
[27] lumi_2.14.1
[28] Biobase_2.22.0
[29] BiocGenerics_0.8.0

loaded via a namespace (and not attached):
 [1] BSgenome_1.30.0        BiocInstaller_1.12.0   Formula_1.1-1
 [4] GenomicFeatures_1.14.2 Gviz_1.6.0             Hmisc_3.14-0
 [7] KernSmooth_2.23-10     Matrix_1.1-2           R.methodsS3_1.6.1
[10] RColorBrewer_1.0-5     RCurl_1.95-4.1         Rsamtools_1.14.2
[13] XML_3.98-1.1           affy_1.40.0            affyio_1.30.0
[16] annotate_1.40.0        beanplot_1.1           biomaRt_2.18.0
[19] biovizBase_1.10.7      bitops_1.0-6           cluster_1.14.4
[22] codetools_0.2-8        colorspace_1.2-4       dichromat_2.0-0
[25] digest_0.6.4           doRNG_1.5.5            genefilter_1.44.0
[28] genoset_1.14.0         gtable_0.1.2           illuminaio_0.2.0
[31] itertools_0.1-1        labeling_0.2           latticeExtra_0.6-26
[34] mclust_4.2             mgcv_1.7-28            multtest_2.18.0
[37] munsell_0.4.2          nleqslv_2.1            nlme_3.1-113
[40] nor1mix_1.1-4          pkgmaker_0.17.4        plyr_1.8
43] preprocessCore_1.24.0  proto_0.3-10           registry_0.2
[46] reshape_0.8.4          rngtools_1.2.3         rtracklayer_1.22.3
[49] siggenes_1.36.0        splines_3.0.2          stats4_3.0.2
[52] stringr_0.6.2          survival_2.37-7        tools_3.0.2
[55] xtable_1.7-1           zlibbioc_1.8.0

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