BMIQ | R Documentation |
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.
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)
## S4 method for signature 'MethyLumiSet'
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)
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. |
Full details can be found in the reference below. Note: these functions require the RPMM package, not currently a dependency of the wateRmelon package.
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
Andrew Teschendorff, MethyLumiSet method by Leo Schalkwyk Leonard.Schalkwyk@kcl.ac.uk
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.
# library(RPMM)
# data(melon)
# BMIQ(melon,nfit=100)
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