Hi Bioconductor Developers and Users,

I've a question regard dmpFinder method in Minfi package 1.8.9,
in particular for shrinkVar argument.

I usually calculate dmp both set shrinkVar=TRUE both set shrinkVar=FALSE
with the following method calls:


1) dmpOne <- dmpFinder(myMvalue.byMyGenomicRatioSet,
pheno=myPhenoData$Sample_Group, type="categorical", shrinkVar=TRUE)

2) dmpTwo <- dmpFinder(myMvalue.byMyGenomicRatioSet,
pheno=myPhenoData$Sample_Group, type="categorical")


and I've a variable number of significance estimates (qval < 0.05) on the
two result objects.


In a dataset of 44 samples I've 207020 significance estimates on dmpOne and
207382 on dmpTwo.
In a dataset of 13 samples I've 67 significance estimates on dmpOne and 19
significance estimates on dmpTwo.
Now in a dataset of 9 samples I've 21 significance estimates on dmpOne and
3102 on dmpTwo.

So I was wondering which of the two result objects should I choose? The one
with the greatest number of significance estimates?
In the manual shrinkVar=TRUE is recommended when sample sizes are small
(<10), but sample sizes refers to the number of samples?
It is also recommended but not "required".


Thanks in advance,
Giovanni

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




***** sessionInfo *****

R version 3.0.1 (2013-05-16)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
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LC_TIME=en_GB.UTF-8
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LC_MESSAGES=en_GB.UTF-8
 [7] LC_PAPER=C                 LC_NAME=C
LC_ADDRESS=C
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8
LC_IDENTIFICATION=C

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

other attached packages:
 [1] IMA_3.1.2
 [2] dplR_1.5.9
 [3] preprocessCore_1.24.0
 [4] bioDist_1.34.0
 [5] KernSmooth_2.23-12
 [6] MASS_7.3-31
 [7] limma_3.18.13
 [8] WriteXLS_3.5.0
 [9] doParallel_1.0.8
[10] lumi_2.14.2
[11] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
[12] IlluminaHumanMethylation450kmanifest_0.4.0
[13] minfi_1.8.9
[14] bumphunter_1.2.0
[15] locfit_1.5-9.1
[16] iterators_1.0.7
[17] foreach_1.4.2
[18] Biostrings_2.30.1
[19] GenomicRanges_1.14.4
[20] XVector_0.2.0
[21] IRanges_1.20.7
[22] reshape_0.8.5
[23] lattice_0.20-29
[24] Biobase_2.22.0
[25] BiocGenerics_0.8.0
[26] BiocInstaller_1.12.1

loaded via a namespace (and not attached):
 [1] affy_1.40.0            affyio_1.30.0          annotate_1.40.1
AnnotationDbi_1.24.0
 [5] base64_1.1             beanplot_1.1           biomaRt_2.18.0
bitops_1.0-6
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DBI_0.2-7
[13] digest_0.6.4           doRNG_1.6              genefilter_1.44.0
GenomicFeatures_1.14.5
[17] gmp_0.5-11             grid_3.0.1             illuminaio_0.4.0
itertools_0.1-3
[21] Matrix_1.1-3           matrixStats_0.8.14     mclust_4.3
methylumi_2.8.0
[25] mgcv_1.7-29            multtest_2.18.0        nleqslv_2.1.1
nlme_3.1-117
[29] nor1mix_1.1-4          pkgmaker_0.20          plyr_1.8.1
RColorBrewer_1.0-5
[33] Rcpp_0.11.1            RCurl_1.95-4.1         registry_0.2
R.methodsS3_1.6.1
[37] rngtools_1.2.4         Rsamtools_1.14.3       RSQLite_0.11.4
rtracklayer_1.22.7
[41] siggenes_1.36.0        splines_3.0.1          stats4_3.0.1
stringr_0.6.2
[45] survival_2.37-7        tools_3.0.1            XML_3.98-1.1
xtable_1.7-3
[49] zlibbioc_1.8.0

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