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wateRmelon (version 1.16.0)

BMIQ: Beta-Mixture Quantile (BMIQ) Normalisation method for Illumina 450k arrays

Description

BMIQ is an intra-sample normalisation procedure, correcting the bias of type-2 probe values. BMIQ uses a 3-step procedure: (i) fitting of a 3-state beta mixture model, (ii) transformation of state-membership probabilities of type2 probes into quantiles of the type1 distribution, and (iii) a conformal transformation for the hemi-methylated probes. Exact details can be found in the reference below.

Usage

BMIQ(beta.v, design.v, nL = 3, doH = TRUE, nfit = 50000, th1.v = c(0.2, 0.75), th2.v = NULL, niter = 5, tol = 0.001, plots = TRUE, sampleID = 1, pri=TRUE) "BMIQ"(beta.v, nL=3, doH=TRUE, nfit=5000, th1.v=c(0.2,0.75), th2.v=NULL, niter=5, tol=0.001, plots=FALSE, pri=FALSE ) CheckBMIQ(beta.v, design.v, pnbeta.v)

Arguments

beta.v
vector consisting of beta-values for a given sample, or a MethyLumiSet or MethylSet containing multiple samples. For the MethyLumiSet and MethylSet methods, this is the only required argument, and the function will be run on each sample.
design.v
corresponding vector specifying probe design type (1=type1,2=type2). This must be of the same length as beta.v and in the same order.
nL
number of states in beta mixture model. 3 by default. At present BMIQ only works for nL=3.
doH
perform normalisation for hemimethylated type2 probes. These are normalised using an empirical conformal transformation and also includes the left-tailed type2 methylated probes since these are not well described by a beta distribution. By default TRUE.
nfit
number of probes of a given design type to use for the fitting. Default is 50000. Smaller values (~10000) will make BMIQ run faster at the expense of a small loss in accuracy. For most applications, 5000 or 10000 is ok.
th1.v
thresholds used for the initialisation of the EM-algorithm, they should represent buest guesses for calling type1 probes hemi-methylated and methylated, and will be refined by the EM algorithm. Default values work well in most cases.
th2.v
thresholds used for the initialisation of the EM-algorithm, they should represent buest guesses for calling type2 probes hemi-methylated and methylated, and will be refined by the EM algorithm. By default this is null, and the thresholds are estimated based on th1.v and a modified PBC correction method.
niter
maximum number of EM iterations to do. This number should be large enough to yield good fits to the type1 distribution. By default 5.
tol
tolerance convergence threshold for EM algorithm. By default 0.001.
plots
logical specifying whether to plot the fits and normalised profiles out. By default TRUE.
sampleID
the ID of the sample being normalised.
pri
logical: print verbose progress information?
pnbeta.v
BMIQ normalised profile.

Value

Default method: A list with following entries:
nbeta
the normalised beta-profile for the sample
class1
the assigned methylation state of type1 probes
class2
the assigned methylation state of type2 probes
av1
the mean beta-values for the nL states for type1 probes
av2
the mean beta-values for the nL states for type2 probes
hf
the estimated "Hubble" dilation factor used in the normalisation of hemi-methylated probes
th1
estimated thresholds for calling unmethylated and methylated type1 probes
th2
estimated thresholds for calling unmethylated and methylated type2 probes
MethyLumiSet method: A methyLumiSet object

Details

Full details can be found in the reference below. Note: these functions require the RPMM package, not currently a dependency of the wateRmelon package.

References

Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A Beta-Mixture Quantile Normalisation method for correcting probe design bias in Illumina Infinium 450k DNA methylation data. Bioinformatics. 2012 Nov 21.

Examples

Run this code

library(RPMM)
data(melon)
BMIQ(melon,nfit=100)

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