Glasbey, Chris

Khondoker, Mizanur

Worton., Bruce

University of East Anglia digital repository

Package ‘multiscan’September 2, 2016Version 1.32.0Date 2008-06-24Title R package for combining multiple scansDepends R (>= 2.3.0)Imports Biobase, utilsbiocViews Microarray, PreprocessingAuthor Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>, Chris Glasbey, Bruce Worton.Maintainer Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>Description Estimates gene expressions from several laser scans of the same microarrayLicense GPL (>= 2)NeedsCompilation yesR topics documented:multiscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1murine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4plot.multiscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Index 7multiscan Combining multiple laser scans of microarray dataDescriptionEstimates gene expressions from multiple laser scans of microarrays using non-linear functionalregression model with additive plus multiplicative errors.Usagemultiscan(data, initial = NULL, na.rm = TRUE, verbose = FALSE, control = list())12 multiscanArgumentsdata A numeric matrix or data frame containing the intensity data of a single microar-ray scanned at multiple (two or more) scanner settings. For dual channel arrays,multiscan should be run on each channel of data separately. The number ofrows (n) is equal to the number of spots/probes on the array, and the number ofcolumns (m) equals the number of scans. Columns will be arranged in order ofscanner’s sensitivity before fitting the model. Replicated probes on the array aretreated as individual spots.initial A vector of length m+2 to be used as initial values for the scanning effects(β2, · · · , βm) and scale (σ1, σ2, ν) parameters. If it is NULL (default), the ini-tial values are determined from the data.na.rm Logical. Should missing values be removed? Defaults to TRUE.verbose Logical. If TRUE, some intermediate results are printed as the iteration proceeds.control A list of control parameters. See ‘Details’.DetailsThe function implements the method of Khondoker et. al. (2006) for combining multiple laserscans of microarrays. This function is computationally slow and memory-intensive. That is dueto the nested iteration loops of the numerical optimization of the likelihood function involving alarge number (n +m + 2) of parameters. The optimization uses an alternating algorithm with theNelder-Mead simplex method (Nelder and Mead, 1965) in the inner loops. The function multiscandirectly uses the C function nmmin, the internal code used in the general-purpose optimization tooloptim, for implementing the Nelder-Mead simplex method. For large data sets with many tens ofthousands of probes, it is recommended to consider first fitting the model using a random subset(e.g. 10,000 rows) of the data matrix, and then using the estimated scanning effects and scaleparameters obtained as initial values for fitting the model to the full data set.The control is a list of arguments. The users can change/supply any of the following components:trace Indicator (0 or 1) of tracing information of Nelder-Mead algorithm. If 1, tracing informa-tion on the progress of the optimization is produced. Because Nelder-Mead may be callledthousands of times during the estimation process, setting trace = 1 will print too muchinformation very rapidly, which may not be useful. Defaults to 0.gmaxit The maximum number of global iterations. Defaults to 150.maxit The maximum number of Nelder-Mead iterations. Defaults to 5000.reltol Relative convergence tolerance of Nelder-Mead. The algorithm stops if it is unable toreduce the value by a factor of reltol * (abs(val) + reltol) at a step. Defaults to 1e-5.globaltol Convergence tolerance of the outer (alternating) iteration. The estimation process con-verges if the gain in loglikelihood from one complete cycle of the outer iteration is less thanglobaltol. Defaults to 1e-10.alpha, beta, gamma Scaling parameters for the Nelder-Mead method. alpha is the reflection factor(default 1.0), beta the contraction factor (0.5) and gamma the expansion factor (2.0).multiscan 3ValueReturns an object of class multiscan with componentscall The call of the multiscan function.beta A vector of length m containing the maximum likelihood estimates of the scan-ning effects, the first component fixed at 1.scale A vector of length 3 containing the maximum likelihood estimates of the scaleparameters σ1, σ2, andν.mu A vector of length n containing the estimated gene expressions.data A matrix of the input data with columns rearranged in order of scanner’s sensi-tivity.fitted A matrix of the fitted model on the data.sdres A matrix of the standardised residuals.outerit Number of global iterations completed.gconv, conv, convmuInteger convergence codes.gconv Indicator of global convergence. 0 indicates successful convergence, 1indicates premature termination.conv Convergence codes for the Nelder-Mead simplex method in the last globaliteration while updating scanning effects and scale parameters. 0 for suc-cessful convergence, 1 indicates that the iteration limit maxit had beenreached, 10 indicates degeneracy of the Nelder-Mead simplex method.convmu Convergence codes for the Nelder-Mead simplex method in the lastglobal iteration while updating the gene expression parameters. This is aninteger vector of length n where each component takes the value 0, 1, or 10depending on whether the Nelder-Mead simplex method successfully con-verged, reached iteration limit maxit or produced degeneracy respectivelywhile updating the corresponding gene expression parameter.outerit Number of global iterations completed.loglf Value of the loglikelihood function at convergence (gconv=0). NA if not con-verged (gconv=1).ReferencesKhondoker, M. R., Glasbey, C. A. and Worton, B. J. (2006). Statistical estimation of gene expres-sion using multiple laser scans of microarrays. Bioinformatics 22, 215–219.Nelder, J. A. and Mead, R. (1965). A simplex method for function minimization. The ComputerJournal 7 308–313.See AlsoA web interface, created by David Nutter of Biomathematics & Statistics Scotland (BioSS), basedon the original Fortran code written by Khondoker et al. (2006) is available at http://www.bioss.ac.uk/ktshowcase/create.cgi.Although it uses the same algorithm, results from the web interface may not be exactly identical tothat of multiscan as it uses a different (non-free IMSL routine) implementation of Nelder-Meadsimplex.4 murineExamples## load the multiscan librarylibrary(multiscan)## load the murine data set included in multiscan packagedata(murine)murine[1:10, ] ## see first few rows of data## fit the model on murine data with default optionsfit <- multiscan(murine)fit## plot the fitted modelplot(fit)## get the estimated gene expressionsgene.exprs <- fit$mu## see more details as iteration progressesfit1 <- multiscan(murine, verbose = TRUE)fit1murine A subset of murine macrophage gene expression data from Khondokeret al.(2006).DescriptionThis data set contains a subset of 1000 rows of murine macrophage array1 data (Khondoker et al.,2006). The data set has 4 columns corresponding to 4 laser scans of one channel of a microarrayscanned at four different scanner settings. This data set is used in an example to illustrate the use ofmultiscan package.Usagedata(murine)FormatA 1000 by 4 data frame.ReferencesKhondoker, M. R., Glasbey, C. A. and Worton, B. J. (2006). Statistical estimation of gene expres-sion using multiple laser scans of microarrays. Bioinformatics 22, 215–219.plot.multiscan 5plot.multiscan Plot fitted model and standardised residuals for a multiscan objectDescriptionThis function provides plots for fitted model and standardised residual for multiscan fitted objects.Usage## S3 method for class 'multiscan'plot(x, residual=FALSE,...)Argumentsx a multiscan fitted object.residual Logical. Should residuals be plotted instead of the fitted model?. Defaults toFALSE.... Further arguments passed to the plot function.DetailsFitted model on the input data, after rescaling by the corresponding scanning effects, is plottedagainst the estimated gene expressions. Standardised residuals for each scan of data are plottedagainst the rank of estimated gene expressions.ValueReturns either one plot of the fitted model (residual=FALSE) on the input data or m plots of theresiduals (residual=TRUE) corresponding to each scan of data.ReferencesKhondoker, M. R., Glasbey, C. A. and Worton, B. J. (2006). Statistical estimation of gene expres-sion using multiple laser scans of microarrays. Bioinformatics 22, 215–219.See AlsomultiscanExamplesdata(murine)fit<-multiscan(murine)## plot the fitted model6 plot.multiscanplot(fit)## plot the residualsop<-par(mfrow=c(2,2))plot(fit, residual=TRUE)par(op)Index∗Topic datasetsmurine, 4∗Topic nonlinearmultiscan, 1∗Topic optimizemultiscan, 1∗Topic regressionplot.multiscan, 5multiscan, 1, 5murine, 4optim, 2plot.multiscan, 57

multiscan: R package for combining multiple scans

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multiscan: R package for combining multiple scans

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