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
**************************************
**************************************
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
**************************************
**************************************
[[alternative HTML version deleted]]
Hi Allegra and Giovanni
The parameters of identifySigDMR need to be tuned based on your
dataset.
The default parameters was designed for the cell lines with clear
difference between groups. For tissue samples, these parameters need
to be
less stringent, especially the diffTh parameter.
Also, I strongly recommend visually checking your data. You can export
the
methylation data using export.methyGenoSet, and then visualize it in
IGV.
Pan
On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice
<giovcalice@gmail.com>wrote:
> 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
> **************************************
> **************************************
> 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
>
> **************************************
> **************************************
>
[[alternative HTML version deleted]]
Hi Pan and Giovanni,
Thanks for your replies, and for the advice on data visualization.
I've found very little documentation for these functions, so I don't
have a good feeling for the parameters and how they should be set. How
is diffTh defined, and what is its range? Do lower diffTh values
correspond to lower stringency? Do you have any tips for translating
the data visualization into meaningful parameters?
Thank so much!
Allegra
_________________________________
Allegra A. Petti, Ph.D.
The Genome Institute
4444 Forest Park Ave.
St. Louis, MO 63108
apetti@genome.wustl.edu
allegra.conbrio@post.harvard.edu
On Feb 25, 2014, at 11:23 AM, Pan Du <dupan.mail@gmail.com> wrote:
> Hi Allegra and Giovanni
>
> The parameters of identifySigDMR need to be tuned based on your
dataset. The default parameters was designed for the cell lines with
clear difference between groups. For tissue samples, these parameters
need to be less stringent, especially the diffTh parameter.
> Also, I strongly recommend visually checking your data. You can
export the methylation data using export.methyGenoSet, and then
visualize it in IGV.
>
> Pan
>
>
>
> On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice
<giovcalice@gmail.com> wrote:
> 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.th argument 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
> **************************************
> **************************************
> 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
>
> **************************************
> **************************************
>
____
This email message is a private communication. The information
transmitted, including attachments, is intended only for the person or
entity to which it is addressed and may contain confidential,
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[[alternative HTML version deleted]]
Hi Pan and Giovanni,
I realized I have one more question. I'm currently using methyAnalysis
starting from idat files, but I'd also like to be able to start from a
tab-delimited matrix of beta values. I couldn't figure out how to work
with (and annotate) beta values in any of the R packages that produce
MethyLumiM objects. Do you have any procedures for this?
Thank you again,
Allegra
_________________________________
Allegra A. Petti, Ph.D.
The Genome Institute
4444 Forest Park Ave.
St. Louis, MO 63108
apetti@genome.wustl.edu
allegra.conbrio@post.harvard.edu
On Feb 25, 2014, at 11:23 AM, Pan Du <dupan.mail@gmail.com> wrote:
> Hi Allegra and Giovanni
>
> The parameters of identifySigDMR need to be tuned based on your
dataset. The default parameters was designed for the cell lines with
clear difference between groups. For tissue samples, these parameters
need to be less stringent, especially the diffTh parameter.
> Also, I strongly recommend visually checking your data. You can
export the methylation data using export.methyGenoSet, and then
visualize it in IGV.
>
> Pan
>
>
>
> On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice
<giovcalice@gmail.com> wrote:
> 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.th argument 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
> **************************************
> **************************************
> 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
>
> **************************************
> **************************************
>
____
This email message is a private communication. The information
transmitted, including attachments, is intended only for the person or
entity to which it is addressed and may contain confidential,
privileged, and/or proprietary material. Any review, duplication,
retransmission, distribution, or other use of, or taking of any action
in reliance upon, this information by persons or entities other than
the intended recipient is unauthorized by the sender and is
prohibited. If you have received this message in error, please contact
the sender immediately by return email and delete the original message
from all computer systems. Thank you.
[[alternative HTML version deleted]]
Hi Allegra
In order to get the MethyLumiM object, you must have the methylated
and
unmethylated probe intensities in your tab-delimited files. The beta-
values
were basically calculated from the methylated and unmethylated probe
intensities. I recommend using .IDAT files if you have them because it
includes all the information.
As for your question about diffTh parameter, you can think it as the
log2-foldChange threshold, which is similar as what expression
analysis
always do.
There are couple of reasons why visually check the methylation data
(without smoothing) is important. Here is just list a couple of them:
1. The location of DMRs, because methylation regulation of transcript
expression is very much chromosome location dependent. DMRs near TSS
and
CpG-islands are more likely to be involved in negative regulation of
transcript expression.
2. Check whether there is consistent behavior of nearby probes (if
there
are) because methylation pattern tends to be locally correlated.
3. Globally check the methylation difference across samples. This may
help
the quality assessment of the samples, or identify whether there
global-hyper or hypo methylation events.
Pan
On Tue, Feb 25, 2014 at 9:47 AM, Allegra A. Petti
<apetti@genome.wustl.edu>wrote:
> Hi Pan and Giovanni,
>
> I realized I have one more question. I'm currently using
methyAnalysis
> starting from idat files, but I'd also like to be able to start from
a
> tab-delimited matrix of beta values. I couldn't figure out how to
work with
> (and annotate) beta values in any of the R packages that produce
MethyLumiM
> objects. Do you have any procedures for this?
>
> Thank you again,
> Allegra
> _________________________________
> Allegra A. Petti, Ph.D.
>
> The Genome Institute
> 4444 Forest Park Ave.
> St. Louis, MO 63108
> apetti@genome.wustl.edu
> allegra.conbrio@post.harvard.edu
>
> On Feb 25, 2014, at 11:23 AM, Pan Du <dupan.mail@gmail.com> wrote:
>
> Hi Allegra and Giovanni
>
> The parameters of identifySigDMR need to be tuned based on your
dataset.
> The default parameters was designed for the cell lines with clear
> difference between groups. For tissue samples, these parameters need
to be
> less stringent, especially the diffTh parameter.
> Also, I strongly recommend visually checking your data. You can
export the
> methylation data using export.methyGenoSet, and then visualize it in
IGV.
>
> Pan
>
>
>
> On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice
<giovcalice@gmail.com>wrote:
>
>> 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
>> **************************************
>> **************************************
>> 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
>>
>> **************************************
>> **************************************
>>
>
>
>
> ____ This email message is a private communication. The information
> transmitted, including attachments, is intended only for the person
or
> entity to which it is addressed and may contain confidential,
privileged,
> and/or proprietary material. Any review, duplication,
retransmission,
> distribution, or other use of, or taking of any action in reliance
upon,
> this information by persons or entities other than the intended
recipient
> is unauthorized by the sender and is prohibited. If you have
received this
> message in error, please contact the sender immediately by return
email and
> delete the original message from all computer systems. Thank you.
>
[[alternative HTML version deleted]]
Hi Pan,
Thank you very much for your helpful response. I really appreciate it!
--Allegra
_________________________________
Allegra A. Petti, Ph.D.
The Genome Institute
4444 Forest Park Ave.
St. Louis, MO 63108
apetti@genome.wustl.edu
allegra.conbrio@post.harvard.edu
On Feb 25, 2014, at 5:51 PM, Pan Du <dupan.mail@gmail.com> wrote:
> Hi Allegra
>
> In order to get the MethyLumiM object, you must have the methylated
and unmethylated probe intensities in your tab-delimited files. The
beta-values were basically calculated from the methylated and
unmethylated probe intensities. I recommend using .IDAT files if you
have them because it includes all the information.
>
> As for your question about diffTh parameter, you can think it as the
log2-foldChange threshold, which is similar as what expression
analysis always do.
>
> There are couple of reasons why visually check the methylation data
(without smoothing) is important. Here is just list a couple of them:
> 1. The location of DMRs, because methylation regulation of
transcript expression is very much chromosome location dependent. DMRs
near TSS and CpG-islands are more likely to be involved in negative
regulation of transcript expression.
> 2. Check whether there is consistent behavior of nearby probes (if
there are) because methylation pattern tends to be locally correlated.
> 3. Globally check the methylation difference across samples. This
may help the quality assessment of the samples, or identify whether
there global-hyper or hypo methylation events.
>
>
> Pan
>
>
> On Tue, Feb 25, 2014 at 9:47 AM, Allegra A. Petti
<apetti@genome.wustl.edu> wrote:
> Hi Pan and Giovanni,
>
> I realized I have one more question. I'm currently using
methyAnalysis starting from idat files, but I'd also like to be able
to start from a tab-delimited matrix of beta values. I couldn't figure
out how to work with (and annotate) beta values in any of the R
packages that produce MethyLumiM objects. Do you have any procedures
for this?
>
> Thank you again,
> Allegra
> _________________________________
> Allegra A. Petti, Ph.D.
>
> The Genome Institute
> 4444 Forest Park Ave.
> St. Louis, MO 63108
> apetti@genome.wustl.edu
> allegra.conbrio@post.harvard.edu
>
> On Feb 25, 2014, at 11:23 AM, Pan Du <dupan.mail@gmail.com> wrote:
>
>> Hi Allegra and Giovanni
>>
>> The parameters of identifySigDMR need to be tuned based on your
dataset. The default parameters was designed for the cell lines with
clear difference between groups. For tissue samples, these parameters
need to be less stringent, especially the diffTh parameter.
>> Also, I strongly recommend visually checking your data. You can
export the methylation data using export.methyGenoSet, and then
visualize it in IGV.
>>
>> Pan
>>
>>
>>
>> On Tue, Feb 25, 2014 at 8:31 AM, Giovanni Calice
<giovcalice@gmail.com> wrote:
>> 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.th argument 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
>> **************************************
>> **************************************
>> 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
>>
>> **************************************
>> **************************************
>>
>
>
> ____ This email message is a private communication. The information
transmitted, including attachments, is intended only for the person or
entity to which it is addressed and may contain confidential,
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in reliance upon, this information by persons or entities other than
the intended recipient is unauthorized by the sender and is
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>
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the intended recipient is unauthorized by the sender and is
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[[alternative HTML version deleted]]
Hi Giovanni,
I got results using some arbitrarily chosen lenient parameters. I
don't
know yet how appropriate these parameters are for your data or mine,
but
in case you want to try them, I used the following command:
allDMRInfo <- identifySigDMR(dmrResult, p.adjust.method="fdr",
pValueTh=0.05, fdrTh=0.1, diffTh=0.1)
(My data set has 56 samples, but in my uninformed opinion, it doesn't
seem like 8-12 samples is too few.)
Best,
Allegra
On 02/25/2014 10:31 AM, Giovanni Calice wrote:
> 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.th
> <http: p.value.detection.th=""> argument 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
> **************************************
> **************************************
> Date: Mon, 24 Feb 2014 14:52:58 -0800 (PST)
> From: "Allegra Petti [guest]" <guest@bioconductor.org> <mailto:guest@bioconductor.org>>
> To: bioconductor@r-project.org <mailto:bioconductor@r-project.org>,
> apetti@genome.wustl.edu <mailto:apetti@genome.wustl.edu>
> Cc: methyAnalysis Maintainer <dupan.mail@gmail.com> <mailto:dupan.mail@gmail.com>>
> Subject: [BioC] methyAnalysis for DMR identification
> Message-ID: <20140224225258.99BC91468A7@mamba.fhcrc.org
> <mailto: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
>
> **************************************
> **************************************
____
This email message is a private communication. The information
transmitted, including attachments, is intended only for the person or
entity to which it is addressed and may contain confidential,
privileged, and/or proprietary material. Any review, duplication,
retransmission, distribution, or other use of, or taking of any action
in reliance upon, this information by persons or entities other than
the intended recipient is unauthorized by the sender and is
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[[alternative HTML version deleted]]
Hi Allegra,
I have already tried to filter with the outcome of No significant CpG-
sites
identified.
Most likely it is the small number of dataset size that
influences the results.
I'll follow Pan's advice to visualize-check data in IGV
and I'm waiting for other samples of the next Illumina chip-array by
my
colleagues
in such way to execute methyAnalysis pipeline on a dataset with a
doubled
size (20-24),
so finally have significant CpG-sites.
Best,
Giovanni
2014-02-25 19:11 GMT+01:00 Allegra Petti <apetti@genome.wustl.edu>:
> Hi Giovanni,
>
> I got results using some arbitrarily chosen lenient parameters. I
don't
> know yet how appropriate these parameters are for your data or mine,
but in
> case you want to try them, I used the following command:
>
> allDMRInfo <- identifySigDMR(dmrResult, p.adjust.method="fdr",
> pValueTh=0.05, fdrTh=0.1, diffTh=0.1)
>
> (My data set has 56 samples, but in my uninformed opinion, it
doesn't seem
> like 8-12 samples is too few.)
>
> Best,
> Allegra
>
>
> On 02/25/2014 10:31 AM, Giovanni Calice wrote:
>
> 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
> **************************************
> **************************************
> 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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> **************************************
>
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