\name{get.probe.noise.estimates} \Rdversion{1.1} \alias{get.probe.noise.estimates} \title{Fetch probe-level noise estimates from an rpa object} \description{Provides probe-level estimates of noise, as given by the RPA algorithm.} \usage{get.probe.noise.estimates(rpa.res, sets = NULL, normalization = NULL, verbose = FALSE)} \arguments{ \item{rpa.res }{An rpa object.} \item{sets }{Probesets to check.} \item{normalization }{Normalization method for probe noise estimates.} The higher the value, the higher the probe-level noise. By default, the estimated probe-level variances (sigma2) of the RPA model are returned. Other options include: "withinset.weights": inverse of probe-wise weights used to summarize the probe-level observations into probeset-level signal "withinset.relative": probe-wise standard deviations versus standard deviation of the probeset-level signal d. This compensates for the effect that the detected probe-level noise is typically coupled with overall signal levels. "withinset.categorical": indexes the probes according to their reliability within each probeset. Probes with higher indices are more noisy. \item{verbose }{Print progress information during computation.} } \details{Provides probe-specific noise estimates. The normalization options are included to improve comparability across probesets.} \value{A list. Each element corresponds to one probeset (of the input object). The element lists noise estimates for each probe within the probeset.} \references{Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays. Lahti et al., TCBB/IEEE. See http://www.cis.hut.fi/projects/mi/software/RPA/ } \author{Leo Lahti } \seealso{RPA.pointestimate} \examples{ ## Load example data set require(affydata) data(Dilution) ## Compute RPA rpa.results <- RPA.pointestimate(Dilution, set = "1000_at") noise <- get.probe.noise.estimates(rpa.results) } \keyword{ methods }