SWAN normalization for HumanMethylation450K - minfi

Hi everyone,

I'm currently attempting to adapt the SWAN normalization scripts found in
the Minfi package for use with MethyLumiM. I noticed this line in the
"preprocessSWAN" script and I'm not sure what this is doing.
bg <- bgIntensitySwan(rgSet)

Here's the bgIntesitySWAN function:
bgIntensitySwan <- function (rgSet)
{
    grnMed <- colMedians(getGreen(rgSet)[getControlAddress(rgSet,
        controlType = "NEGATIVE"), ])
    redMed <- colMedians(getRed(rgSet)[getControlAddress(rgSet,
        controlType = "NEGATIVE"), ])
    return(rowMeans(cbind(grnMed, redMed)))
}

It seems to be calculating the median NEGATIVE signals of each sample in
each color channel separately then calculating the average per sample. How
is this information used in the quantile normalization? I initially thought
the entire SWAN process would only depend on the sample itself and will not
be affected by other samples since it's correcting the discordance between
Type I and II probes within each sample.

Thanks in advance for any insights into this!
Cheers,
Lucia

*Lucia Lam, B Sc
*

Lab Manager and Research Assistant for Dr. Michael S. Kobor

*Centre for Molecular Medicine and Therapeutics (CMMT) ****- Researching
Life to Change Lives*

*University of British Columbia***
  *

*> sessionInfo()
R version 2.15.0 (2012-03-30)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

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

other attached packages:
 [1] IlluminaHumanMethylation450k.db_1.4.6
org.Hs.eg.db_2.7.1
 [3] RSQLite_0.11.1
DBI_0.2-5
 [5] AnnotationDbi_1.18.1
Biostrings_2.24.1
 [7] minfi_1.2.0
GenomicRanges_1.8.6
 [9] IRanges_1.14.3
reshape_0.8.4
[11] plyr_1.7.1
lattice_0.20-6
[13] lumi_2.8.0
nleqslv_1.9.3
[15] methylumi_2.2.0
ggplot2_0.9.1
[17] reshape2_1.2.1
scales_0.2.1
[19] Biobase_2.16.0
BiocGenerics_0.2.0

loaded via a namespace (and not attached):
 [1] affy_1.34.0           affyio_1.24.0         annotate_1.34.0
 [4] beanplot_1.1          bigmemory_4.2.11      BiocInstaller_1.4.7
 [7] bit_1.1-8             bitops_1.0-4.1        BSgenome_1.24.0
[10] codetools_0.2-8       colorspace_1.1-1      crlmm_1.14.3
[13] dichromat_1.2-4       digest_0.5.2          DNAcopy_1.30.0
[16] ellipse_0.3-7         ff_2.2-7              foreach_1.4.0
[19] genefilter_1.38.0     genoset_1.6.0         grid_2.15.0
[22] hdrcde_2.16           iterators_1.0.6       KernSmooth_2.23-7
[25] labeling_0.1          limma_3.12.0          MASS_7.3-18
[28] Matrix_1.0-6          matrixStats_0.5.0     mclust_3.4.11
[31] memoise_0.1           mgcv_1.7-17           multtest_2.12.0
[34] munsell_0.3           mvtnorm_0.9-9992      nlme_3.1-104
[37] nor1mix_1.1-3         oligoClasses_1.18.0   preprocessCore_1.18.0
[40] proto_0.3-9.2         R.methodsS3_1.4.2     RColorBrewer_1.0-5
[43] RCurl_1.91-1.1        Rsamtools_1.8.5       rtracklayer_1.16.1
[46] siggenes_1.30.0       splines_2.15.0        stats4_2.15.0
[49] stringr_0.6           survival_2.36-14      tools_2.15.0
[52] XML_3.9-4.1           xtable_1.7-0          zlibbioc_1.2.0

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