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Measurement Error Microarray

Appl. M., Durbin B. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. New York: Cambridge University Press; 2003. this content

Let ℛ be centred and scaled residuals of model (1) where the Xi’s were replaced by sample means and g was replaced by g̃. The second subtle issue is that, because of sampling error, naive application of nonparametric regression methods to the reduced data leads to inconsistent estimators. NCBISkip to main contentSkip to navigationResourcesHow ToAbout NCBI AccesskeysMy NCBISign in to NCBISign Out PMC US National Library of Medicine National Institutes of Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web DiscussionThe performance of both SIMEX methods depends on the approximate extrapolants used in practice and their potential may not be fully realized; more research on better extrapolants is necessary.

The system returned: (22) Invalid argument The remote host or network may be down. In practice, one may compare performances of various methods using simulations similar to those in §5, and select a method accordingly.A referee suggested that caution needs to be exercised when using We thus only need to show (14)–(15).

The Analysis of Gene Expression Data: Methods and Software. Nonparametric regression in the presence of measurement error. Optimal shrinkage estimation of variances with applications to microarray data analysis. Figure 1 shows the true function, observations and the naive estimator ĝN.

For any fixed b and j, we have the following asymptotic expansion (Carroll et al., 1998): g^b,j(x0,ζ)=gPS(x0,ζ)+(τh2/2)gPS(2)(x0,ζ)+{nv(x0,ζ)}−1∑i=1nKh{Wb,i(j)(ζ)−x0}Q[Yi,j,Wb,i(j)(ζ),gPS{Wb,i(j)(ζ),ζ}]+op{h2+log(n)(nh)−1/2}.(10)Then, for fixed B,g^PS(x0,ζ)=gPS(x0,ζ)+(τh2/2)gPS(2)(x0,ζ)+{Bmnv(x0,ζ)}−1∑b=1B∑j=1m∑i=1nKh{Wb,i(j)(ζ)−x0}Q[Yi,j,Wb,i(j)(ζ),gPS{Wb,i(j)(ζ),ζ}]+op{h2+log(n)(nh)−1/2}.(11)The expansion (11) is justified as long as B is finite. Statist. New York: Cambridge University Press; 2003. http://biomet.oxfordjournals.org/content/95/2/437.refs A.

The variance is a function of the mean and our goal is to estimate the variance-mean function g nonparametrically.2. Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. E. Bioinformatics 2003;19:1945-51.

Ratio statistics of gene expression levels and applications to microarray data analysis. In the case of a constant variance function, g(x) ≡ g0, ĝS approaches g(x0)−fX(2)(x0)/{m(m−1)fX(x0)}. The performance of both SIMEX methods depends on approximations to the exact extrapolants. Figure 3 plots sample variance versus sample mean, naive estimators, direct SIMEX estimators and permutation SIMEX estimators.

Chen Y, Dougherty ER, Bittner ML. news Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Therefore, care needs to be taken to derive consistent estimators of the variance function. CrossRefMedlineWeb of ScienceGoogle Scholar Rocke D.

We used a grid of K = 100 equally spaced points in the range of to perform extrapolation. You can change your cookie settings at any time. The system returned: (22) Invalid argument The remote host or network may be down. have a peek at these guys A., Crainiceanu C.

Bioinformatics 2006;22:1111-21. Generated Thu, 20 Oct 2016 14:00:52 GMT by s_wx1011 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.7/ Connection Ratio statistics of gene expression levels and applications to microarray data analysis.

Improved statistical tests for differential gene expression by shrinking variance components estimates.

Replicated microarray data. Figure 4 shows the histogram and density estimate of , the function g̃ and the Q-Q plot of ℛ. DNA microarray experiments: Biological and technological aspects. CrossRefMedlineGoogle Scholar Jain N., Thatte J., Braciale T., Ley K., O'Connell M., Lee J.

Your cache administrator is webmaster. BMC Biotechnol. 2001;1:1–8. [PMC free article] [PubMed]Leung Y, Cavalieri D. We approximate IMSE by MSE=(nK)−1Δ∑k=1K∑i=1n{g^(xi)−g(xi)}2f∼X(xi) where the xi’s are grid points, Δ is the grid length, and f̃X is the kernel density estimate shown in Fig. 4(a). check my blog We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators.

CrossRefGoogle Scholar Ruppert D., Wand M., Carroll R. Local-pooled error test for identifying differentially expressed genes with a small number of replicated microarrays. The system returned: (22) Invalid argument The remote host or network may be down. Your cache administrator is webmaster.

R., Bittner M. A simulation extrapolation method for parametric measurement error models. A. Since Si is an unbiased estimator of g(Xi), if X were observable a locally constant regression estimator is g^(x0)=n−1∑i=1nKh(Xi−x0)Sin−1∑i=1nKh(Xi−x0),(2) where K(·) is a symmetric density function, h is the bandwidth and

Carroll Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A. The system returned: (22) Invalid argument The remote host or network may be down. Then another estimating function is(Y−X)2λ−aλgλ(X).(5)In general, λ = 1/3 or 1/2 adds more robustness with little loss of efficiency. We note that, rather than offering formal conclusions, the fits here serve the purpose of illustration only.Fig. 3Tumour study.

Squared biases, variances and mean squared errors.6. Please try the request again. Bioinformatics. 2003;19:1945–51. [PubMed]Kamb A, Ramaswami A.