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

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

New normalization methods using support vector machine quantile regression approach in microarray analysis

There are many sources of systematic variations in cDNA microarray experiments which affect the measured gene expression levels. Print-tip lowess normalization is widely used in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. However, print-tip lowess normalization performs poorly in situations where error variability for each gene is heterogeneous over intensity ranges. We first develop support vector machine quantile regression (SVMQR) by extending support vector machine regression (SVMR) for the estimation of linear and nonlinear quantile regressions, and then propose some new print-tip normalization methods based on SVMR and SVMQR. We apply our proposed normalization methods to previous cDNA microarray data of apolipoprotein AI-knockout (apoAI-KO) mice, diet-induced obese mice, and genistein-fed obese mice. From our comparative analyses, we find that our proposed methods perform better than the existing print-tip lowess normalization method.

One-to-One Comparison of Graphite-Blended Negative Electrodes Using Silicon Nanolayer-Embedded Graphite versus Commercial Benchmarking Materials for High-Energy Lithium-Ion Batteries

While existing carbonaceous anodes for lithium-ion batteries (LIBs) are approaching a practical capacitive limit, Si has been extensively examined as a potential alternative because it shows exceptional gravimetric capacity (3579 mA h g-1) and abundance. However, the actual implementation of Si anodes is impeded by difficulties in electrode calendering processes and requirements for excessive binding and conductive agents, arising from the brittleness, large volume expansion (&gt;300%), and low electrical conductivity (1.56 ?? 10-3 S m-1) of Si. In one rational approach to using Si in high-energy LIBs, mixing Si-based materials with graphite has attracted attention as a feasible alternative for next-generation anodes. In this study, graphite-blended electrodes with Si nanolayer-embedded graphite/carbon (G/SGC) are demonstrated and detailed one-to-one comparisons of these electrodes with industrially developed benchmarking samples are performed under the industrial electrode density (&gt;1.6 g cc-1), areal capacity (&gt;3 mA h cm-2), and a small amount of binder (3 wt%) in a slurry. Because of the favorable compatibility between SGC and conventional graphite, and the well-established structural features of SGC, great potential is envisioned. Since this feasible study utilizes realistic test methods and criteria, the rigorous benchmarking comparison presents a comprehensive understanding for developing and characterizing Si-based anodes for practicable high-energy LIBs

Confronting Issues of the Practical Implementation of Si Anode in High-Energy Lithium-Ion Batteries

Over 20 years, Si has been investigated as a promising alternative to conventional graphite because of its high specific capacity and proper working voltage. As numerous strategies have demonstrated their improved electrochemical properties by addressing the intrinsic challenges of Si anode, the practical investigation with a full cell has been regarded as an important task to verify their feasibilities. In this Perspective, we discuss key issues in the practical implementation of the Si anode in the high-energy full cell. With the target of improvement in the volumetric energy density, the comprehensive overview of an electrochemical cell design for Si anodes is presented with its influence on electrochemical properties. Moreover, we highlight the electrode swelling issues and the capacity fading of the Si anode, which is pronounced in the full cell rather than in the half cell. Finally, we offer insights regarding the potential future directions in the development of the Si anode for high-energy lithium-ion batterie

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Wind stilling has been observed in many regions across the Northern Hemisphere; however, the related mechanisms are not well understood. Analyses of the wind speed variations in South Korea during 1993–2015 in this study reveal that the annual-mean surface wind speeds at rural stations have increased by up to 0.41 m s ^−1 decade ^−1 , while those at urban stations have decreased by up to −0.63 m s ^−1 decade ^−1 . The local wind speed variations are found to be negatively correlated with the population density at the corresponding observation sites. Gustiness analyses show the increase in local surface roughness due to urbanization can explain the observed negative wind speed trends at urban stations as the urbanization effect overwhelms the positive wind speed trend due to climate change. The observed negative wind speed trend in urban areas are not found in the regional climate model simulations in the Coordinated Regional Climate Downscaling Experiment—East Asia (CORDEX-EA) as these models do not take into account the impact of urbanization on wind variations during the period. This study suggests that urbanization can play an important role in the recent wind stilling in rapidly developing regions such as South Korea. Our results suggest that future climate projections in CORDEX-EA may overestimate wind speeds in urban areas, and that future regional climate projections need to consider the effects of urbanization for a more accurate projection of wind speeds

Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information