\name{RPA.pointestimate} \Rdversion{1.1} \alias{RPA.pointestimate} \title{Computing point estimate for the model parameters for all probe sets.} \description{Computes point estimate} \usage{RPA.pointestimate(abatch, sets = NULL, myseed = 101, priors = NULL, epsilon = 1e-2, cind = 1, sigma2.method = "robust", d.method = "fast", verbose = TRUE, bg.method = "rma", normalization.method = "quantiles.robust", cdf = NULL, alpha = NULL, beta = NULL, affinity.method = "rpa")} \arguments{ \item{abatch }{An AffyBatch object.} \item{sets }{Specifies the probesets for which RPA estimates will be computed. Default: all probe sets.} \item{myseed }{Specifies the random seed.} \item{priors }{An 'rpa.priors' object. Can be used to set user-specified priors for the model parameters. Not applicable for sigma2.method = "var".} \item{epsilon }{Convergence tolerance. The iteration is deemed converged when the change in the d parameter is < epsilon.} \item{cind }{Specifies which array in abatch is used as a reference in computing probe-level differential expression.} \item{sigma2.method }{ Optimization method for sigma2 (probe-specific variances). "robust": (default) update sigma2 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 sigma2 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 sigma2 with posterior mean "mean": update sigma2 with posterior mean "var": update sigma2 with variance around d. Applies the fact that sigma2 cost function converges to variance with large sample sizes. } \item{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. } \item{verbose }{ Print progress information during computation. Default: TRUE.} \item{bg.method }{ Specify background correction method. Default: "rma". See bgcorrect.methods() for other options.} \item{normalization.method }{ Specify quantile normalization method. Default: "pmonly". See normalize.methods(Dilution) for other options.} \item{cdf }{ Specify an alternative CDF environment. Default: none. } \item{alpha, beta }{Prior parameters for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with sigma2.method 'var'. Scalar alpha and beta specify an identical inverse Gamma prior for all probes, which regularizes the solution. Probe-specific priors can be set with the 'priors' parameter.} \item{affinity.method}{ Model details for affinity estimation explained in function source code. "rpa": Assuming affinity parameters are zero on average, and the deviation from zero is determined by estimated probe-level noise parameters. This gives higher weight (smaller affinity) for more reliable probes also in affinity estimation. Heuristic solution, which aims to fit probe-level signal in real data domain as close to the reliable probes as possible. "zeromean": assumes that probe affinities sum to zero. Analogous to model assumptions in RMA. Gives equal weights for all probes in affinity estimation. We expect this to be less optimal than weighting probes by their general reliability. } } \details{Calculates RPA estimates of probe reliability and differential expression between the user-specified reference array (cind) and the other arrays in the data set. The model assumes P observations for each transcript target (i.e. a probeset) with Gaussian noise which is specific for each probe (variance is specified by sigma2). The mean (affinity) parameters of the Gaussian noise model cancel out in calculating probe-level differential expression. RPA.pointestimate gives a point estimate for d and sigma2. The 'prior' parameter is not applicable with sigma2.method = "var". The d.method = "fast" is recommended with large sample size.} \value{ An instance of class 'rpa'. This is an extended list containing the following elements: \item{d }{A matrix of probesets x arrays. Specifies the estimated 'true' underlying differential gene expression signal over the arrays (vs. the reference array 'cind') for each investigated probeset. Note that the reference array is not included.} \item{sigma2 }{A list. Each element corresponds to a probeset, and contains a vector that gives the estimated variance for each probe in that probeset. This corresponds to the parameter tau^2 in the vignette and manuscript.} \item{cind }{Specifies which of the arrays in the abatch (the affybatch object to be analyzed) has been used as the reference for computing probe-level differential expression.} \item{affinity}{Probe affinity effects.} \item{sets }{A character vector listing the investigated probesets.} } \references{Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays. Lahti et al., TCBB/IEEE, 2011. See citation("RPA") for details.} \author{Leo Lahti \email{leo.lahti@iki.fi}} \note{sigma2.method = "robust" and d.method = "fast" are recommended. With small sample size and informative priors, d.method = "basic" may be preferable.} \seealso{rpa.plot, rpa, set.priors, rpa2eset, RPA.preprocess, AffyBatch, rpa.fit, estimate.affinities} \examples{ ## Load example data set #require(affydata) #data(Dilution) ## Compute RPA for whole data set ## ... slow, not executed here # rpa.results <- RPA.pointestimate(Dilution) ## Visualize the results for one of the probe sets #rpa.plot(set, rpa.results) } \keyword{ methods }