\name{computeCopynumber}
\alias{computeCopynumber}
\title{Computes copy number}
\description{
  
  A function that transforms the quantile-normalized fluorescence
  intensities of the polymorphic and nonpolymorphic probes to a copy
  number scale.  
  
}
\usage{
computeCopynumber(chrom, A, B, calls, conf, NP, plate, MIN.OBS=1, 
envir, P, DF.PRIOR = 50, CONF.THR = 0.99, bias.adj=FALSE,
priorProb, gender=NULL, SNR, SNRmin, seed=123, cdfName="genomewidesnp6",
verbose=TRUE, ...)
}
\arguments{
  \item{chrom}{Chromosome (an integer).  Use 23 for X and 24 for Y.}
  \item{A}{The A allele intensities from \code{snprma}}
  \item{B}{The B allele intensities from \code{snprma}}
  \item{calls}{The genotype calls from \code{crlmm}}
  \item{conf}{The genotype confidence scores from \code{crlmm}}
  \item{NP}{The quantile normalized intensities of the nonpolymorphic probes}
  \item{plate}{The bach variable.  Should be the same length as the
    number of columns in A}
  \item{MIN.OBS}{Integer: The minimum number of observations in a genotype
    cluster for which a SNP is deemed complete.}
  \item{envir}{An environment to save intermediate objects}
  \item{P}{Mainly for debugging a particular plate/batch.}
  \item{DF.PRIOR}{The degrees of freedom for the prior.  Higher numbers
    will shrink the variance and correlation more.}
  \item{CONF.THR}{A threshold for the genotype confidence scores.
    Genotypes with scores below the threshold are ignored when computing
  SNP-specific  within-genotype estimates of location and scale.}
  \item{bias.adj}{Logical: whether to adjust the location and scale
    parameters to account for biases due to common copy number
    variants.  This is a SNP-specific adjustment.  Parameters for
    background and slope must have already been estimated and available
    from the environment variable.}
  \item{priorProb}{Numerical vector of length 4.  The prior probability
    of each copy number state (0, 1, 2, 3, and 4). The default is a
    uniform prior.  Ignored if bias.adj=FALSE}
  \item{gender}{Gender of subjects. If not specified, we predict the
    gender from the X chromosome.}
  \item{SNR}{Signal to noise ratio from crlmm.}
  \item{SNRmin}{The minimum value for the SNR -- we suggest 5. Samples
    with SNR below SNRmin are excluded.}
  \item{seed}{Seed used for random samples}
  \item{cdfName}{Annotation package }
  \item{verbose}{Logical: verbose output}
  \item{\dots}{Currently ignored}
}

\details{
  
  Parameters for copy number are estimated using a linear model based on
  the diallelic genotype calls.  No training data is used to estimate
  model parameters. Therefore, this function requires at least 10
  samples to estimate copy number.  For small sample sizes (e.g., 10 -
  30 samples), this function will impute model parameters for a large
  number of loci and the precision of the estimates will be reduced.
  
  Key assumption:

  - we assume that the median copy number at any given locus is two for
    each batch.  This assumption may not be appropriate for many
    datasets (e.g., a cancer dataset without normals processed in the
    same batch).

    The developmental version of this package available from
    Bioconductor has many improvements to this function.
  
}

\value{

All objects created by this function are stored in the environment
  passed to this function.  In addition, each of the elements are
  specific to the chromosome(s) specified by the argument \code{chrom}.
  For instance the element \code{A} is the matrix of quantile-normalized
  intensities for the A-allele on chromosome(s) \code{chrom}.  The
  element of this environment are as follows

  \item{A}{Matrix of quantile-normalized intensities for the A-allele}
  \item{B}{Matrix of quantile-normalized intensities for the A-allele}
  \item{CA}{Copy number estimate for the A-allele (x 100)} 
  \item{CB}{Copy number estimate for the B-allele (x 100)}
  \item{calls}{CRLMM genotype calls (AA=1, AB=2, BB=3)}
  \item{chrom}{Integer(s) indicating the chromosome(s)} 
  \item{cnvs}{Names of the nonpolymorphic probes.  These are the
  rownames of \code{NP} and \code{CT}.}
  \item{conf}{CRLMM confidence scores for the genotypes: 'round(-1000*log2(1-p))'}
  \item{corr}{Correlation of the A and B alleles for genotypes AB}
  \item{corrA.BB}{Correlation of A and B alleles for genotypes BB}
  \item{corrB.AA}{Correlation of A and B alleles for genotypes AA}
  \item{CT}{Copy number estimates for nonpolymorphic probe.  See
  \code{cnvs} for the rownames.}
  \item{CT.sds}{Standard deviation estimates for \code{CT}}
  \item{npflags}{Flags for the nonpolymorphic probes.}
  \item{Ns}{The number of observations for each genotype/plate}
  \item{nuA}{Background/cross-hyb for the A allele (plate- and locus-specific)}
  \item{nuB}{Background/cross-hyb for the B allele (plate- and locus-specific)}
  \item{nuT}{Background for the nonpolymorphic probes (plate- and locus-specific)}
  \item{phiA}{Slope for the A allele (plate- and locus-specific)}
  \item{phiB}{Slope for the B allele (plate- and locus-specific)}
  \item{phiT}{Slope for the nonpolymorphic probes (plate- and locus-specific)}
  \item{plate}{Factor indicating batch (same length as number of cel files)}
  \item{sig2A}{Variance estimate for the A-allele signal (plate- and locus-specific)}
  \item{sig2B}{Variance estimate for the B-allele signal (plate- and locus-specific)}
  \item{sig2T}{Variance estimate for the nonpolymorphic signal (plate- and locus-specific)}
  \item{snpflags}{Flags for polymorphic probes}
  \item{snps}{Rownames for \code{A}, \code{B}, \code{CA}, \code{CB}, ...}
  \item{sns}{ Sample names -- the column names for \code{A}, \code{B}, ...}
  \item{steps}{Steps completed. For internal use.}
  \item{tau2A}{Variance estimate for the B-allele background/cross-hyb (plate- and locus-specific)}
  \item{tau2B}{Variance estimate for the B-allele background/cross-hyb (plate- and locus-specific)}
}
\references{Nothing yet.}
\author{Rob Scharpf}
\keyword{manip}