A permutation-based multiple testing method for time-course microarray experiments

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey <it>et al</it>. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.</p> <p>Results</p> <p>In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey <it>et al</it>. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the <it>Caenorhabditis elegans </it>dauer developmental data.</p> <p>Conclusion</p> <p>Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.</p

Prediction of a time-to-event trait using genome wide SNP data

BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data

Multiple testing for gene sets from microarray experiments

<p>Abstract</p> <p>Background</p> <p>A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome.</p> <p>Results</p> <p>In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.</p> <p>Conclusions</p> <p>Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.</p

Novel twin-roll-cast Ti/Al clad sheets with excellent tensile properties

Pure Ti or Ti alloys are recently spot-lighted in construction industries because they have excellent resistance to corrosions, chemicals, and climates as well as various coloring characteristics, but their wide applications are postponed by their expensiveness and poor formability. We present a new fabrication process of Ti/Al clad sheets by bonding a thin Ti sheet on to a 5052 Al alloy melt during vertical-twin-roll casting. This process has merits of reduced production costs as well as improved tensile properties. In the as-twin-roll-cast clad sheet, the homogeneously cast microstructure existed in the Al alloy substrate side, while the Ti/Al interface did not contain any reaction products, pores, cracks, or lateral delamination, which indicated the successful twin-roll casting. When this sheet was annealed at 350 degrees C-600 degrees C, the metallurgical bonding was expanded by interfacial diffusion, thereby leading to improvement in tensile properties over those calculated by a rule of mixtures. The ductility was also improved over that of 5052-O Al alloy (25%) or pure Ti (25%) by synergic effect of homogeneous deformation due to excellent Ti/Al bonding. This work provides new applications of Ti/Al clad sheets to lightweight-alloy clad sheets requiring excellent formability and corrosion resistance as well as alloy cost saving.112Ysciescopu

SNP Selection in Genome-Wide Association Studies via Penalized Support Vector Machine with MAX Test

One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity

First results from the HAYSTAC axion search

The axion is a well-motivated cold dark matter (CDM) candidate first postulated to explain the absence of $CP$ violation in the strong interactions. CDM axions may be detected via their resonant conversion into photons in a "haloscope" detector: a tunable high-$Q$ microwave cavity maintained at cryogenic temperature, immersed a strong magnetic field, and coupled to a low-noise receiver. This dissertation reports on the design, commissioning, and first operation of the Haloscope at Yale Sensitive to Axion CDM (HAYSTAC), a new detector designed to search for CDM axions with masses above $20$ $\mu\mathrm{eV}$. I also describe the analysis procedure developed to derive limits on axion CDM from the first HAYSTAC data run, which excluded axion models with two-photon coupling $g_{a\gamma\gamma} \gtrsim 2\times10^{-14}$ $\mathrm{GeV}^{-1}$, a factor of 2.3 above the benchmark KSVZ model, over the mass range $23.55 < m_a < 24.0$ $\mu\mathrm{eV}$. This result represents two important achievements. First, it demonstrates cosmologically relevant sensitivity an order of magnitude higher in mass than any existing direct limits. Second, by incorporating a dilution refrigerator and Josephson parametric amplifier, HAYSTAC has demonstrated total noise approaching the standard quantum limit for the first time in a haloscope axion search.Comment: Ph.D. thesis. 346 pages, 58 figures. A few typos corrected relative to the version submitted to ProQues

Robust test method for time-course microarray experiments

<p>Abstract</p> <p>Background</p> <p>In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For many of these experiments, the scientific aim is the identification of genes for which the trajectories depend on an experimental or phenotypic factor. There is an extensive recent body of literature on statistical methodology for addressing this analytical problem. Most of the existing methods are based on estimating the time-course trajectories using parametric or non-parametric mean regression methods. The sensitivity of these regression methods to outliers, an issue that is well documented in the statistical literature, should be of concern when analyzing microarray data.</p> <p>Results</p> <p>In this paper, we propose a robust testing method for identifying genes whose expression time profiles depend on a factor. Furthermore, we propose a multiple testing procedure to adjust for multiplicity.</p> <p>Conclusions</p> <p>Through an extensive simulation study, we will illustrate the performance of our method. Finally, we will report the results from applying our method to a case study and discussing potential extensions.</p