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- Lönnstedt, Ingrid, et al.
(author)
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Hierarchical Bayes models for cDNA microarray gene expression
- 2005
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In: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 6:2, s. 279-291
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Journal article (peer-reviewed)abstract
- cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.
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| 4. |
- Stjernqvist, Susann, et al.
(author)
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A continuous-index hidden Markov jump process for modeling DNA copy number data
- 2009
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In: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 10:4, s. 773-778
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Journal article (peer-reviewed)abstract
- The number of copies of DNA in human cells can be measured using array comparative genomic hybridization (aCGH), which provides intensity ratios of sample to reference DNA at genomic locations corresponding to probes on a microarray. In the present paper, we devise a statistical model, based on a latent continuous-index Markov jump process, that is aimed to capture certain features of aCGH data, including probes that are unevenly long, unevenly spaced, and overlapping. The model has a continuous state space, with 1 state representing a normal copy number of 2, and the rest of the states being either amplifications or deletions. We adopt a Bayesian approach and apply Markov chain Monte Carlo (MCMC) methods for estimating the parameters and the Markov process. The model can be applied to data from both tiling bacterial artificial chromosome arrays and oligonucleotide arrays. We also compare a model with normal distributed noise to a model with t-distributed noise, showing that the latter is more robust to outliers.
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| 5. |
- Sundberg, Rolf, 1942-, et al.
(author)
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Statistical modeling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia
- 2006
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In: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 7:1, s. 130-144
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Journal article (peer-reviewed)abstract
- Aspects of experimental design, statistical modeling, and statistical inference in case-control real-time reverse transcription-polymerase chain reaction (RT-PCR) assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia related. Real-time RT-PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required. A basic multivariate (for several genes jointly) random-effects analysis of covariance model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first to average over the intraindividual replicates. This has consequences for the influence of plate effects. Topics also discussed are testing for a significant mean disease effect, differential coregulation, and the difficulty of identifying genes affected in only a subgroup of cases.
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| 6. |
- Ängquist, Lars, et al.
(author)
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Improving the calculation of statistical significance in genome-wide scans
- 2005
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In: Biostatistics. - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 6:4, s. 520-538
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Journal article (peer-reviewed)abstract
- Calculations of the significance of results from linkage analysis can be performed by simulation or by theoretical approximation, with or without the assumption of perfect marker information. Here we concentrate on theoretical approximation. Our starting point is the asymptotic approximation formula presented by Lander and Kruglyak (1995, Nature Genetics, 11, 241-247), incorporating the effect of finite marker spacing as suggested by Feingold et al. (1993, American Journal of Human Genetics, 53, 234-251). We consider two distinct ways in which this formula can be improved. Firstly, we present a formula for calculating the crossover rate rho for a pedigree of general structure. For a pedigree set, these values may then be weighted into an overall crossover rate which can be used as input to the original approximation formula. Secondly, the unadjusted p-value formula is based on the assumption of a Normally distributed nonparametric linkage (NPL) score. This leads to conservative or anticonservative p-values of varying magnitude depending on the pedigree set structure. We adjust for non-Normality by calculating the marginal distribution of the NPL score under the null hypothesis of no linkage with an arbitrarily small error. The NPL score is then transformed to have a marginal standard Normal distribution and the transformed maximal NPL score, together with a slightly corrected value of the overall crossover rate, is inserted into the original formula in order to calculate the p-value. We use pedigrees of seven different structures to compare the performance of our suggested approximation formula to the original approximation formula, with and without skewness correction, and to results found by simulation. We also apply the suggested formula to two real pedigree set structure examples. Our method generally seems to provide improved behavior, especially for pedigree sets which show clear departure from Normality, in relation to the competing approximations.
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