Gene selection and clustering for time-course
and dose–response microarray experiments using order-restricted inference

Peddada, Shyamal D.; Lobenhofer, Edward K.; Li, Leping; Afshari, Cynthia A.; Weinberg, Clarice R.; Umbach, David M.

doi:10.1093/bioinformatics/btg093

Cited by 181 publications

(169 citation statements)

References 15 publications

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“…The goal is to select significant genes and cluster them by their expression patterns over the six time points, namely, 1, 4, 12, 24, 36 and 48 hours after the breast cancer cells were treated with estradiol. For each gene, we establish a null hypothesis versus an alternative as in Peddada et al (2003). We then applied the order-restricted inference methodology of Peddada et al (2003) and Peddada et al (2005) to derive the p-value corresponding to each test needed for using the proposed methodology.…”

Section: Illustrationmentioning

confidence: 99%

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Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing

Peddada

2008

Statistical Applications in Genetics and Molecular Biology

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It is a common practice to use resampling methods such as the bootstrap for calculating the p-value for each test when performing large scale multiple testing. The precision of the bootstrap p-values and that of the false discovery rate (FDR) relies on the number of bootstraps used for testing each hypothesis. Clearly, the larger the number of bootstraps the better the precision. However, the required number of bootstraps can be computationally burdensome, and it multiplies the number of tests to be performed. Further adding to the computational challenge is that in some applications the calculation of the test statistic itself may require considerable computation time. As technology improves one can expect the dimension of the problem to increase as well. For instance, during the early days of microarray technology, the number of probes on a cDNA chip was less than 10,000. Now the Affymetrix chips come with over 50,000 probes per chip. Motivated by this important need, we developed a simple adaptive bootstrap methodology for large scale multiple testing, which reduces the total number of bootstrap calculations while ensuring the control of the FDR. The proposed algorithm results in a substantial reduction in the number of bootstrap samples. Based on a simulation study we found that, relative to the number of bootstraps required for the BenjaminiHochberg (BH) procedure, the standard FDR methodology which was the proposed methodology achieved a very substantial reduction in the number of bootstraps. In some cases the new algorithm required as little as 1/6th the number of bootstraps as the conventional BH procedure. Thus, if the conventional BH procedure used 1,000 bootstraps, then the proposed method required only 160 bootstraps. This methodology has been implemented for time-course/dose-response data in our software, ORIOGEN, which is available from the authors upon request.

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Section: Illustrationmentioning

confidence: 99%

“…Among these two lists, 199 genes are common and all of the top 50 genes selected by Peddada et al (2003) were also selected by the proposed algorithm. The average number of bootstraps required by the proposed algorithm was only 2,175 in comparison to B N =10,000.…”

Section: Illustrationmentioning

confidence: 99%

Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing

Peddada

2008

Statistical Applications in Genetics and Molecular Biology

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“…In addition, templates can be formed by the model for possible trajectories for which established cell cycle genes are either unknown or unavailable in the data, features that rule out averaging. We must caution, however, that correlation-based methods can be misleading (22).…”

Section: Discussionmentioning

confidence: 99%

A random-periods model for expression of cell-cycle genes

Liu

Umbach

Peddada

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

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We propose a nonlinear regression model for quantitatively analyzing periodic gene expression in studies of experimentally synchronized cells. Our model accounts for the observed attenuation in cycle amplitude by a simple and biologically plausible mechanism. We represent the expression level for each gene as an average across a large number of cells. For a given cell-cycle gene, we model its expression in each cell in the culture as following the same sinusoidal function except that the period, which in any individual cell must be the same for all cell-cycle genes, varies randomly across cells. We model these random periods by using a lognormal distribution. The variability in period causes the measured amplitude of the cyclic expression trajectory to attenuate over time as cells fall increasingly out of synchrony. Gene-specific parameters include initial amplitude and phase angle. Applying the model to data from Whitfield et al. bootstrap test ͉ gene expression ͉ microarray ͉ nonlinear regression E xperimental protocols that arrest cells in vitro at a particular phase of the cell cycle and then release them in a synchronized way allow detailed study of the cycling process. In conjunction with such experiments, cDNA microarray technology allows investigators to assess the temporal expression patterns of thousands of genes simultaneously. Gene-expression studies in yeast (1-3) and in cultured human cells (4, 5) have revealed that expression levels for cell-cycle genes vary periodically and have amplitudes that attenuate through time (Fig. 1). The observed attenuation is generally attributed to cells in the cultures falling increasingly out of synchrony through time.In cultures of homogeneous cells released from a block on cycling, asynchrony can arise from at least two mechanisms. Cells throughout the culture can differ slightly in the exact timing of their arrest or release, or cells can differ slightly in the duration of their individual cycles. The former mechanism leaves asynchrony constant through time, so only the latter mechanism, where asynchrony increases, produces the characteristic attenuation. Such attenuation may hold biologic interest in itself. The rate of attenuation (i.e., the variation in the duration of the cell cycle across cells) may vary by strain of organism or by cell type (e.g., across tumors with differing metastatic potential). Some influences of cell characteristics on the scheduling of the cell cycle are already known. For instance, cell-size distribution in yeast mutants is related to time spent in the late G 1 phase (6).Few methods proposed for the analysis of cell-cycle expression data address attenuation explicitly. For example, several investigators used a sinusoidal template to identify periodically expressed genes but did not explicitly account for attenuation (2, 5). The basic single-pulse model (SPM) proposed by Zhao et al. (7) addresses asynchrony by assuming that the cell-specific timings with which individual cells in a culture reach a given observation point have a norma...

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“…The performance of statistical inference can be improved significantly if this information can be appropriately utilized in the inferential procedure [57]. Order-restricted statistical inference is an efficient tool by using ordering information.…”

Section: Order-restricted Inference With Applicationsmentioning

confidence: 99%

A project of applied statistical methods in China: review and outlook

Geng

Guo

Shi

et al. 2008

Statistics and Its Interface

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In this paper, we review and explore some important aspects of a key project on applied statistical methods supported by the National Natural Science Foundation of China. Our discussion focuses on (1) statistical inference under order restriction, (2) structural learning of graphical models, and (3) statistical analysis on haplotypes and recombination fractions in genetics and some research results on Hardy-Weinberg equilibrium (HWE) and admixture linkage disequilibrium (ALD).

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Gene selection and clustering for time-course and dose–response microarray experiments using order-restricted inference

Cited by 181 publications

References 15 publications

Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing

Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing

A random-periods model for expression of cell-cycle genes

A project of applied statistical methods in China: review and outlook

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