rpa.complete: Complete RPA preprocessing
In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

Description Usage Arguments Details Value Author(s) References Examples

RPA preprocessing, also returns probe parameters.

rpa.complete(abatch = NULL, sets = NULL, epsilon = 0.01,
  tau2.method = "robust", d.method = "fast", verbose = FALSE,
  bg.method = "rma", normalization.method = "quantiles.robust",
  cdf = NULL, cel.files = NULL, cel.path = NULL,
  probe.parameters = list(), mc.cores = 1,
  summarize.with.affinities = FALSE)

`abatch`	An AffyBatch object.
`sets`	Probesets for which RPA will be computed.
`epsilon`	Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
`tau2.method`	Optimization method for tau2 (probe-specific variances). This parameter is denoted by tau^2 in the vignette and manuscript "robust": (default) update tau2 by posterior mean, regularized by informative priors that are identical for all probes (user-specified by setting scalar values for alpha, beta). This regularizes the solution, and avoids overfitting where a single probe obtains infinite reliability. This is a potential problem in the other tau2 update methods with non-informative variance priors. The default values alpha = 2; beta = 1 are used if alpha and beta are not specified. "mode": update tau2 with posterior mean "mean": update tau2 with posterior mean "var": update tau2 with variance around d. Applies the fact that tau2 cost function converges to variance with large sample sizes.
`d.method`	Method to optimize d. "fast": (default) weighted mean over the probes, weighted by probe variances The solution converges to this with large sample size. "basic": optimization scheme to find a mode used in Lahti et al. TCBB/IEEE; relatively slow; this is the preferred method with small sample sizes.
`verbose`	Print progress information during computation.
`bg.method`	Specify background correction method. Default: "rma". See bgcorrect.methods() for other options.
`normalization.method`	Specify quantile normalization method. Default: "pmonly". See normalize.methods(Dilution) for other options.
`cdf`	Specify an alternative CDF environment. Default: none.
`cel.files`	List of CEL files to preprocess.
`cel.path`	Path to CEL file directory.
`probe.parameters`	A list, each element corresponding to a probe set. Each probeset element has the following optional elements: affinity (affinity), tau2 (variance), alpha (shape prior), betas (scale prior). Each of these elements contains a vector over the probeset probes, specifying the probe parameters according to the RPA model. If variance is given, it overrides the priors. Can be also used to set user-specified priors for the model parameters. Not used tau2.method = "var". The prior parameters alpha and beta are prior parameters for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with tau2.method 'var'. Scalar alpha and beta specify an identical inverse Gamma prior for all probes, which regularizes the solution. Can be also specified as lists, each element corresponding to one probeset. Can also include quantile.basis
`mc.cores`	Number of cores for parallelized processing.
`summarize.with.affinities`	Use affinity estimates in probe summarization step. Default: FALSE.

RPA preprocessing function. Gives an estimate of the probeset-level mean parameter d of the RPA model, and returns these in an expressionSet object. The choices tau2.method = "robust" and d.method = "fast" are recommended. With small sample size and informative prior, d.method = "basic" may be preferable. For very large expression data collections, see rpa.online function.

List with preprocessed expression matrix, corresponding probe parameters, AffyBatch and CDF

Leo Lahti leo.lahti@iki.fi

See citation("RPA")