crlmmCopynumber: Locus- and allele-specific estimation of copy number

In benilton/crlmm-release: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

Description

Locus- and allele-specific estimation of copy number.

Usage

crlmmCopynumber(object, MIN.SAMPLES=10, SNRMin = 5, MIN.OBS = 1,
	        DF.PRIOR = 50, bias.adj = FALSE,
                prior.prob = rep(1/4, 4), seed = 1, verbose = TRUE,
                GT.CONF.THR = 0.80, MIN.NU = 2^3, MIN.PHI = 2^3,
                THR.NU.PHI = TRUE, type=c("SNP", "NP", "X.SNP", "X.NP"),
                fit.linearModel=TRUE)

Arguments

object	object of class CNSet.
MIN.SAMPLES	'Integer'. The minimum number of samples in a batch. Bathes with fewer than MIN.SAMPLES are skipped. Therefore, samples in batches with fewer than MIN.SAMPLES have NA's for the allele-specific copy number and NA's for the linear model parameters.
SNRMin	Samples with low signal to noise ratios are excluded.
MIN.OBS	For a SNP with with fewer than MIN.OBS of a genotype in a given batch, the within-genotype median is imputed. The imputation is based on a regression using SNPs for which all three biallelic genotypes are observed. For example, assume at at a given SNP genotypes AA and AB were observed and BB is an unobserved genotype. For SNPs in which all 3 genotypes were observed, we fit the model E(mean_BB) = beta0 + beta1*mean_AA + beta2*mean_AB, obtaining estimates; of beta0, beta1, and beta2. The imputed mean at the SNP with unobserved BB is then beta0hat + beta1hat * mean_AA of beta2hat * mean_AB.
DF.PRIOR	The 2 x 2 covariance matrix of the background and signal variances is estimated from the data at each locus. This matrix is then smoothed towards a common matrix estimated from all of the loci. DF.PRIOR controls the amount of smoothing towards the common matrix, with higher values corresponding to greater smoothing. Currently, DF.PRIOR is not estimated from the data. Future versions may estimate DF.PRIOR empirically.
bias.adj	bias.adj is currently ignored (as well as the prior.prob argument). We plan to add this feature back to the crlmm package in the near future. This feature, when TRUE, updated initial estimates from the linear model after excluding samples with a low posterior probability of normal copy number. Excluding samples that have a low posterior probability can be helpful at loci in which a substantial fraction of the samples have a copy number alteration. For additional information, see Scharpf et al., 2010.
prior.prob	This argument is currently ignored. A numerical vector providing prior probabilities for copy number states corresponding to homozygous deletion, hemizygous deletion, normal copy number, and amplification, respectively.
seed	Seed for random number generation.
verbose	Logical.
GT.CONF.THR	Confidence threshold for genotype calls (0, 1). Calls with confidence scores below this theshold are not used to estimate the within-genotype medians. See Carvalho et al., 2007 for information regarding confidence scores of biallelic genotypes.
MIN.NU	numeric. Minimum value for background intensity. Ignored if THR.NU.PHI is FALSE.
MIN.PHI	numeric. Minimum value for slope. Ignored if THR.NU.PHI is FALSE.
THR.NU.PHI	If THR.NU.PHI is FALSE, MIN.NU and MIN.PHI are ignored. When TRUE, background (nu) and slope (phi) coefficients below MIN.NU and MIN.PHI are set to MIN.NU and MIN.PHI, respectively.
type	Character string vector that must be one or more of "SNP", "NP", "X.SNP", or "X.NP". Type refers to a set of markers. See details below
fit.linearModel	Logical. If TRUE, a linear model is fit to estimate the parameters for computing the absolute copy number. If FALSE, we compute the batch-specific, within-genotype median and MAD at polymorphic loci and the median and MAD at nonpolymorphic loci.

Details

We suggest a minimum of 10 samples per batch for using crlmmCopynumber. 50 or more samples per batch is preferred and will improve the estimates.

The functions crlmmCopynumberLD and crlmmCopynumber2 have been deprecated.

The argument type can be used to specify a subset of markers for which the copy number estimation algorithm is run. One or more of the following possible entries are valid: 'SNP', 'NP', 'X.SNP', and 'X.NP'.

'SNP' referers to autosomal SNPs.

'NP' refers to autosomal nonpolymorphic markers.

'X.SNP' refers to SNPs on chromosome X.

'X.NP' refers to autosomes on chromosome X.

However, users must run 'SNP' prior to running 'NP' and 'X.NP', or specify type = c('SNP', 'X.NP').

Value

The value returned by the crlmmCopynumber function depends on whether the data is stored in RAM or whether the data is stored on disk using the R package ff for reading / writing. If uncertain, the first line of the show method defined for CNSet objects prints whether the assayData elements are derived from the ff package in the first line. Specifically,

- if the elements of the batchStaticts slot in the CNSet object have the class "ff_matrix" or "ffdf", then the crlmmCopynumber function updates the data stored on disk and returns the value TRUE.

- if the elements of the batchStatistics slot in the CNSet object have the class 'matrix', then the crlmmCopynumber function returns an object of class CNSet with the elements of batchStatistics updated.

Author(s)

R. Scharpf

References

Carvalho B, Bengtsson H, Speed TP, Irizarry RA. Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics. 2007 Apr;8(2):485-99. Epub 2006 Dec 22. PMID: 17189563.

Carvalho BS, Louis TA, Irizarry RA. Quantifying uncertainty in genotype calls. Bioinformatics. 2010 Jan 15;26(2):242-9.

Scharpf RB, Ruczinski I, Carvalho B, Doan B, Chakravarti A, and Irizarry RA, Biostatistics. Biostatistics, Epub July 2010.

benilton/crlmm-release documentation built on May 12, 2019, 10:59 a.m.