Wen Zhou
Wen Zhou
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Associate Professor of Biostatistics
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Professional overview
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Wen Zhou is an Associate Professor in the Department of Biostatistics at the School of Global Public Health. He received his Ph.D.s in Statistics and Applied Mathematics from the Iowa State University. His research focuses on developing theories and methods for network data analysis, high-dimensional statistics, multiple testing problems, machine learning, and causal inference. He is particularly interested in applications within genomics, genetics, bioinformatics, protein structure modeling, social science, epidemiology, and health policy. Wen serves on the editorial boards of the Statistica Sinica, Journal of Multivariate Analysis, Biometrics, as well as serves as the Editor-in-Chief of Journal of Biopharmaceutical Statistics. He is an elected member of the International Statistical Institute and has been elected as the WNAR program coordinator in 2024.
Before joining NYU, he was an Associate Professor in Statistics at Colorado State University and held a joint appointment in the Department of Biostatistics and Informatics at the Colorado School of Public Health as well as affiliated to the Molecular, Cellular and Integrative Neurosciences (MCIN) Program.
Visit Dr. Zhou's website to learn more about his professional history and accomplishments.
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Education
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BS, Computational Mathematics, Chongqing UniversityPhD, Applied Mathematics, Iowa State UniversityPhD, Statistics, Iowa State University
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Honors and awards
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Elected Member of International Statistical Institute (ISI) (2020)NSF junior faculty travel award (2016)Los Alamos Statistical Sciences Conference Grant (2014)5th place finish in the 14th Data Mining Cup Competition (Team leader of the ISU team) (2013)Chair’s Award on Academic Excellence (2012)Graduate Teaching Excellence Award (2010)Robert J. Lambert Award for Excellence in Applied Mathematics (2008)Professional Advancement Grants (2007)
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Publications
Publications
With random regressors, least squares inference is robust to correlated errors with unknown correlation structure
AbstractZhang, Z., Ding, P., Zhou, W., & Wang, H. (n.d.).Publication year
2025Journal title
BiometrikaVolume
112Issue
1AbstractLinear regression is arguably the most widely used statistical method. With fixed regressors and correlated errors, the conventional wisdom is to modify the variance-covariance estimator to accommodate the known correlation structure of the errors. We depart from existing literature by showing that with random regressors, linear regression inference is robust to correlated errors with unknown correlation structure. The existing theoretical analyses for linear regression are no longer valid because even the asymptotic normality of the least squares coefficients breaks down in this regime. We first prove the asymptotic normality of the t statistics by establishing their Berry-Esseen bounds based on a novel probabilistic analysis of self-normalized statistics. We then study the local power of the corresponding t tests and show that, perhaps surprisingly, error correlation can even enhance power in the regime of weak signals. Overall, our results show that linear regression is applicable more broadly than the conventional theory suggests, and they further demonstrate the value of randomization for ensuring robustness of inference.LARGE-SCALE INFERENCE OF MULTIVARIATE REGRESSION FOR HEAVY-TAILED AND ASYMMETRIC DATA
AbstractSong, Y., Zhou, W., & Zhou, W. X. (n.d.).Publication year
2023Journal title
Statistica SinicaVolume
33Issue
3Page(s)
1831-1852AbstractLarge-scale multivariate regression is a fundamental statistical tool with a wide range of applications. This study considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The challenge that accompanies a large number of tests is the ubiquitous presence of heavy-tailed and/or highly skewed measurement noise, which is the main reason for the failure of conventional least squares-based methods. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features such as heavy tailedness and skewness, which are not visible to least squares methods. The new testing procedure is based on the data-adaptive Huber regression and a new covariance estimator of regression estimates. Under mild conditions, we show that our methods produce consistent estimates of the false discovery proportion. Extensive numerical experiments and an empirical study on quantitative linguistics demonstrate the advantage of the proposed method over many state-of-the-art methods when the data are generated from heavy-tailed and/or skewed distributions.Ten simple rules for managing laboratory information
Berezin, C. T., Aguilera, L. U., Billerbeck, S., Bourne, P. E., Densmore, D., Freemont, P., Gorochowski, T. E., Hernandez, S. I., Hillson, N. J., King, C. R., Köpke, M., Ma, S., Miller, K. M., Moon, T. S., Moore, J. H., Munsky, B., Myers, C. J., Nicholas, D. A., Peccoud, S. J., … Peccoud, J. (n.d.).Publication year
2023Journal title
PLoS computational biologyVolume
19Issue
12Non-asymptotic properties of spectral decomposition of large Gram-type matrices and applications
AbstractZhang, L., Zhou, W., & Wang, H. (n.d.).Publication year
2022Journal title
BernoulliVolume
28Issue
2Page(s)
1224-1249AbstractGram-type matrices and their spectral decomposition are of central importance for numerous problems in statistics, applied mathematics, physics, and machine learning. In this paper, we carefully study the non-asymptotic properties of spectral decomposition of large Gram-type matrices when data are not necessarily independent. Specifically, we derive the exponential tail bounds for the deviation between eigenvectors of the right Gram matrix to their population counterparts as well as the Berry-Esseen type bound for these deviations. We also obtain the non-asymptotic tail bound of the ratio between eigenvalues of the left Gram matrix, namely the sample covariance matrix, and their population counterparts regardless of the size of the data matrix. The documented non-asymptotic properties are further demonstrated in a suite of applications, including the non-asymptotic characterization of the estimated number of latent factors in factor models and relate machine learning problems, the estimation and forecasting of high-dimensional time series, the spectral properties of large sample covariance matrix such as perturbation bounds and inference on the spectral projectors, and low-rank matrix denoising using dependent data.Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model
AbstractKeller, J. P., Zhou, T., Kaplan, A., Anderson, G. B., & Zhou, W. (n.d.).Publication year
2022Journal title
Statistics in MedicineVolume
41Issue
15Page(s)
2745-2767AbstractThe spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.A semiparametric latent factor model for large scale temporal data with heteroscedasticity
AbstractZhang, L., Zhou, W., & Wang, H. (n.d.).Publication year
2021Journal title
Journal of Multivariate AnalysisVolume
186AbstractLarge scale temporal data have flourished in a vast array of applications, and their sophisticated structures, especially the heteroscedasticity among subjects with inter- and intra-temporal dependence, have fueled a great demand for new statistical models. In this paper, with covariate information, we consider a flexible model for large scale temporal data with subject-specific heteroscedasticity. Formally, the model employs latent semiparametric factors to simultaneously account for the subject-specific heteroscedasticity and the contemporaneous and/or serial correlations. The subject-specific heteroscedasticity is modeled as the product of the unobserved factor process and subject's covariate effect, which is further characterized via additive models. For estimation, we propose a two-step procedure. First, the latent factor process and nonparametric loading are recovered through projection-based methods, and following, we estimate the regression components by approaches motivated from the generalized least squares. By scrupulously examining the non-asymptotic rates for recovering the factor process and its loading, we show the consistency and efficiency of estimated regression coefficients in the absence of prior knowledge of latent factor process and subject's covariate effect. The statistical guarantees remain valid even for finite time points that makes our method particularly appealing when the subjects significantly outnumber the observation time points. Using comprehensive simulations, we demonstrate the finite sample performance of our method, which corroborates the theoretical findings. Finally, we apply our method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.Large-scale multiple inference of collective dependence with applications to protein function
AbstractJernigan, R., Jia, K., Ren, Z., & Zhou, W. (n.d.).Publication year
2021Journal title
Annals of Applied StatisticsVolume
15Issue
2Page(s)
902-924AbstractMeasuring the dependence of k ≥ 3 random variables and drawing inference from such higher-order dependences are scientifically important yet challenging. Motivated here by protein coevolution with multivariate categorical features, we consider an information theoretic measure of higher-order dependence. The proposed collective dependence is a symmetrization of differential interaction information which generalizes the mutual information of a pair of random variables. We show that the collective dependence can be easily estimated and facilitates a test on the dependence of k ≥ 3 random variables. Upon carefully exploring the null space of collective dependence, we devise a Classification-Assisted Large scaLe inference procedure to DEtect significant k-COllective DEpendence among d ≥ k random variables, with the false discovery rate controlled. Finite sample performance of our method is examined via simulations. We apply this method to the multiple protein sequence alignment data to study the residue or position coevolution for two protein families, the elongation factor P family and the zinc knuckle family. We identify novel functional triplets of amino acid residues, whose contributions to the protein function are further investigated. These confirm that the collective dependence does yield additional information important for understanding the protein coevolution compared to the pairwise measures.Nonparallel transcriptional divergence during parallel adaptation
AbstractFischer, E. K., Song, Y., Hughes, K. A., Zhou, W., & Hoke, K. L. (n.d.).Publication year
2021Journal title
Molecular ecologyVolume
30Issue
6Page(s)
1516-1530AbstractHow underlying mechanisms bias evolution toward predictable outcomes remains an area of active debate. In this study, we leveraged phenotypic plasticity and parallel adaptation across independent lineages of Trinidadian guppies (Poecilia reticulata) to assess the predictability of gene expression evolution during parallel adaptation. Trinidadian guppies have repeatedly and independently adapted to high- and low-predation environments in the wild. We combined this natural experiment with a laboratory breeding design to attribute transcriptional variation to the genetic influences of population of origin and developmental plasticity in response to rearing with or without predators. We observed substantial gene expression plasticity, as well as the evolution of expression plasticity itself, across populations. Genes exhibiting expression plasticity within populations were more likely to also differ in expression between populations, with the direction of population differences more likely to be opposite those of plasticity. While we found more overlap than expected by chance in genes differentially expressed between high- and low-predation populations from distinct evolutionary lineages, the majority of differentially expressed genes were not shared between lineages. Our data suggest alternative transcriptional configurations associated with shared phenotypes, highlighting a role for transcriptional flexibility in the parallel phenotypic evolution of a species known for rapid adaptation.Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis
AbstractCao, M., Zhou, W., Breidt, F. J., & Peers, G. (n.d.).Publication year
2020Journal title
BiometricsVolume
76Issue
1Page(s)
9-22AbstractExperiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.Equilibrium Distributions of Populations of Biological Species on Networks of Social Sites
AbstractWang, M., Zhou, W., & Wu, Z. (n.d.).Publication year
2019Journal title
Journal of Biological DynamicsVolume
13Page(s)
74-98AbstractWe investigate the problem of how a population of biological species would distribute over a given network of social sites so that their social contacts through the connected sites can be maximized (or minimized). This problem has applications in modelling the behaviours of social (or solitary) species such as the development of social groups in human society and the spread of solitary animals in distant habitats. We show that this problem can be formulated as an evolutionary game, with the equilibrium state of the game corresponding to a strategy for choosing the residing sites, each with a certain probability, or equivalently, to a distribution of the population on these sites. The game has a symmetric payoff matrix, and can therefore be analyzed via the solution of a corresponding quadratic programme: An equilibrium strategy of the game is a KKT point of the quadratic programme, which may be a local maximizer, local minimizer, or saddle point, but it is evolutionarily stable if and only if it is a strict local maximizer. In general, with a goal to maximize the social contacts, the species tend to spread on network sites where there are dense connections such as a complete subnetwork or in other words, a network clique. We show that at equilibrium, the population may or may not distribute on a network clique, but the stability of the equilibrium state does depend on the structure of the selected subnetwork. In particular, we show that the distribution of the population on a maximal network clique is evolutionarily stable unless the clique is ‘attached’ to another clique of the same or larger size, when the population may be able to switch or expand to the neighbouring clique to increase or at least maintain its total amount of contacts. However, the distribution of the population on a non-clique subnetwork is always evolutionarily unstable or weakly evolutionarily stable at the very best, for the population can always move away from its current distribution without decreasing its total amount of contacts. We conclude that the strategies to spread on maximal network cliques are not only equilibrium strategies but also evolutionarily more stable than those on non-clique subnetworks, thus theoretically reaffirming the evolutionary advantages of joining social cliques in social networks for social species.A single dose polyanhydride-based vaccine platform promotes and maintains anti-GnRH antibody titers
AbstractSchaut, R. G., Brewer, M. T., Hostetter, J. M., Mendoza, K., Vela-Ramirez, J. E., Kelly, S. M., Jackman, J. K., Dell’Anna, G., Howard, J. M., Narasimhan, B., Zhou, W., & Jones, D. E. (n.d.).Publication year
2018Journal title
VaccineVolume
36Issue
7Page(s)
1016-1023AbstractTraditionally, vaccination strategies require an initial priming vaccination followed by an antigen boost to generate adequate immunity. Here we describe vaccination against a self-peptide for reproductive sterilization utilizing a three-stage vaccine platform consisting of gonadotropin releasing hormone multiple antigenic peptide (GnRH-MAP) as a soluble injection coupled with subcutaneous administration of polyanhydride-immobilized GnRH-MAP and a cyto-exclusive implant containing GnRH-MAP dendrimer-loaded polyanhydride. This strategy generated and maintained cell-mediated and humoral immunity for up to 41 weeks after a single vaccination in mice with enhanced antibody avidity over time. All intact implants had a grossly visible tissue interface with neovascularization and lymphocytic aggregates. Despite detectable immunity, sterility was not achieved and the immune response did not lead to azoospermia in male mice nor prevent estrus and ovulation in female mice. However, the vaccine delivery device is tunable and the immunogen, adjuvants and release rates can all be modified to enhance immunity. This technology has broad implications for the development of long-term vaccination schemes.Transcriptome-wide comparison of selenium hyperaccumulator and nonaccumulator Stanleya species provides new insight into key processes mediating the hyperaccumulation syndrome
AbstractWang, J., Cappa, J. J., Harris, J. P., Edger, P. P., Zhou, W., Pires, J. C., Adair, M., Unruh, S. A., Simmons, M. P., Schiavon, M., & Pilon-Smits, E. A. (n.d.).Publication year
2018Journal title
Plant Biotechnology JournalVolume
16Issue
9Page(s)
1582-1594AbstractTo obtain better insight into the mechanisms of selenium hyperaccumulation in Stanleya pinnata, transcriptome-wide differences in root and shoot gene expression levels were investigated in S. pinnata and related nonaccumulator Stanleya elata grown with or without 20 μm selenate. Genes predicted to be involved in sulphate/selenate transport and assimilation or in oxidative stress resistance (glutathione-related genes and peroxidases) were among the most differentially expressed between species; many showed constitutively elevated expression in S. pinnata. A number of defence-related genes predicted to mediate synthesis and signalling of defence hormones jasmonic acid (JA, reported to induce sulphur assimilatory and glutathione biosynthesis genes), salicylic acid (SA) and ethylene were also more expressed in S. pinnata than S. elata. Several upstream signalling genes that up-regulate defence hormone synthesis showed higher expression in S. pinnata than S. elata and might trigger these selenium-mediated defence responses. Thus, selenium hyperaccumulation and hypertolerance in S. pinnata may be mediated by constitutive, up-regulated JA, SA and ethylene-mediated defence systems, associated with elevated expression of genes involved in sulphate/selenate uptake and assimilation or in antioxidant activity. Genes pinpointed in this study may be targets of genetic engineering of plants that may be employed in biofortification or phytoremediation.Transcriptomic analysis of differentially expressed genes in the molting gland (Y-organ) of the blackback land crab, Gecarcinus lateralis, during molt-cycle stage transitions
AbstractDas, S., Vraspir, L., Zhou, W., Durica, D. S., & Mykles, D. L. (n.d.).Publication year
2018Journal title
Comparative Biochemistry and Physiology - Part D: Genomics and ProteomicsVolume
28Page(s)
37-53AbstractA transcriptome of the Gecarcinus lateralis molting gland (Y-organ or YO) contained 48,590 contiguous sequences (contigs) from intermolt (IM), early premolt (EP), mid premolt (MP), late premolt (LP), and postmolt (PM) stages. The YO is kept in the basal state in IM by molt-inhibiting hormone (MIH)/cyclic nucleotide-dependent signaling. YO activation in EP requires down-regulation of MIH signaling and activation of mechanistic target of rapamycin (mTOR)-dependent protein synthesis. Transition of the YO to the committed state in MP requires activin/transforming growth factor-beta (TGFβ) signaling. YO repression occurs at the end of LP. A total of 28,179 contigs (58%) showed molt stage-specific changes in gene expression. The largest number of differentially-expressed genes (DEGs) were at the IM/EP (16,142 contigs), LP/PM (18,161 contigs), and PM/IM (8290 contigs) transitions. By contrast, the numbers of DEGs were 372 and 1502 contigs for the EP/MP and MP/LP transitions, respectively. DEG analysis of 23 signal transduction pathways showed significant changes in MIH, mTOR, activin/TGFβ Notch, MAP kinase, and Wnt signaling. Down-regulation of MIH signaling genes in premolt is consistent with reduced MIH sensitivity in MP and LP. Up-regulation of mTOR signaling genes in IM and premolt stages is consistent with its role in YO activation and sustained ecdysteroidogenesis. Up-regulation of activin/TGFβ signaling genes in EP and MP is consistent with the role of a myostatin/activin-like factor in YO commitment. Notch, MAP kinase, and Wnt DEG analysis may indicate possible crosstalk with the MIH, mTOR, and activin/TGFβ pathways to integrate other inputs to control YO ecdysteroidogenesis.Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
AbstractChang, J., Zhou, W., Zhou, W. X., & Wang, L. (n.d.).Publication year
2017Journal title
BiometricsVolume
73Issue
1Page(s)
31-41AbstractComparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.On the preservation of cooperation in two-strategy games with nonlocal interactions
AbstractAydogmus, O., Zhou, W., & Kang, Y. (n.d.).Publication year
2017Journal title
Mathematical BiosciencesVolume
285Page(s)
25-42AbstractNonlocal interactions such as spatial interaction are ubiquitous in nature and may alter the equilibrium in evolutionary dynamics. Models including nonlocal spatial interactions can provide a further understanding on the preservation and emergence of cooperation in evolutionary dynamics. In this paper, we consider a variety of two-strategy evolutionary spatial games with nonlocal interactions based on an integro-differential replicator equation. By defining the invasion speed and minimal traveling wave speed for the derived model, we study the effects of the payoffs, the selection pressure and the spatial parameter on the preservation of cooperation. One of our most interesting findings is that, for the Prisoners Dilemma games in which the defection is the only evolutionary stable strategy for unstructured populations, analyses on its asymptotic speed of propagation suggest that, in contrast with spatially homogeneous games, the cooperators can invade the habitat under proper conditions. Other two-strategy evolutionary spatial games are also explored. Both our theoretical and numerical studies show that the nonlocal spatial interaction favors diversity in strategies in a population and is able to preserve cooperation in a competing environment. A real data application in a virus mutation study echoes our theoretical observations. In addition, we compare the results of our model to the partial differential equation approach to demonstrate the importance of including non-local interaction component in evolutionary game models.Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity
AbstractChang, J., Zheng, C., Zhou, W. X., & Zhou, W. (n.d.).Publication year
2017Journal title
BiometricsVolume
73Issue
4Page(s)
1300-1310AbstractIn this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.Testing for high-dimensional white noise using maximum cross-correlations
AbstractChang, J., Yao, Q., & Zhou, W. (n.d.).Publication year
2017Journal title
BiometrikaVolume
104Issue
1Page(s)
111-127AbstractWe propose a new omnibus test for vector white noise using the maximum absolute autocorrelations and cross-correlations of the component series. Based on an approximation by the L∞-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing departure from white noise that is not independent and identically distributed.We illustrate the accuracy and the power of the proposed test by simulation, which also shows that the newtest outperforms several commonly used methods, including the Lagrange multiplier test and the multivariate Box-Pierce portmanteau tests, especially when the dimension of the time series is high in relation to the sample size. The numerical results also indicate that the performance of the new test can be further enhanced when it is applied to pre-transformed data obtained via the time series principal component analysis proposed by J. Chang, B. Guo and Q.Yao (arXiv:1410.2323). The proposed procedures have been implemented in an R package.More powerful tests for sparse high-dimensional covariances matrices
AbstractPeng, L., Chen, S. X., & Zhou, W. (n.d.).Publication year
2016Journal title
Journal of Multivariate AnalysisVolume
149Page(s)
124-143AbstractThis paper considers improving the power of tests for the identity and sphericity hypotheses regarding high dimensional covariance matrices. The power improvement is achieved by employing the banding estimator for the covariance matrices, which leads to significant reduction in the variance of the test statistics in high dimension. Theoretical justification and simulation experiments are provided to ensure the validity of the proposed tests. The tests are used to analyze a dataset from an acute lymphoblastic leukemia gene expression study for an illustration.Optimality and stability of symmetric evolutionary games with applications in genetic selection
AbstractHuang, Y., Hao, Y., Wang, M., Zhou, W., & Wu, Z. (n.d.).Publication year
2015Journal title
Mathematical Biosciences and EngineeringVolume
12Issue
3Page(s)
503-523AbstractSymmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice
AbstractWang, L., Czedik-Eysenberg, A., Mertz, R. A., Si, Y., Tohge, T., Nunes-Nesi, A., Arrivault, S., Dedow, L. K., Bryant, D. W., Zhou, W., Xu, J., Weissmann, S., Studer, A., Li, P., Zhang, C., LaRue, T., Shao, Y., Ding, Z., Sun, Q., … Brutnell, T. P. (n.d.).Publication year
2014Journal title
Nature BiotechnologyVolume
32Issue
11Page(s)
1158-1164AbstractC4 and C3 photosynthesis differ in the efficiency with which they consume water and nitrogen. Engineering traits of the more efficient C4 photosynthesis into C3 crops could substantially increase crop yields in hot, arid conditions. To identify differences between C4 and C3 photosynthetic mechanisms, we profiled metabolites and gene expression in the developing leaves of Zea mays (maize), a C4 plant, and Oryza sativa (rice), a C3 plant, using a statistical method named the unified developmental model (UDM). Candidate cis-regulatory elements and transcription factors that might regulate photosynthesis were identified, together with differences between C4 and C3 nitrogen and carbon metabolism. The UDM algorithms could be applied to analyze and compare development in other species. These data sets together with community viewers to access and mine them provide a resource for photosynthetic research that will inform efforts to engineer improvements in carbon fixation in economically valuable grass crops.Quantification of egg yolk contamination in egg white using UV/Vis spectroscopy: Prediction model development and analysis
AbstractYao, L., Zhou, W., Wang, T., Liu, M., & Yu, C. (n.d.).Publication year
2014Journal title
Food ControlVolume
43Page(s)
88-97AbstractThe objective of this study was to develop a statistical model for predicting egg yolk contamination level in egg white using a spectroscopic method. Eggs that were stored for 0, 0.5, 1.0, 1.5, 2.5, and 6.5wk at 4°C were manually processed to cleanly separate the yolk from the white. Egg white samples containing 0-0.5% (w/w) of yolk were prepared by adding yolk to the white and further diluting with the pure egg white. Transmission spectra of samples were acquired at 500nm wavelength. The optical absorbance of the "contaminated" egg white samples positively correlated with the yolk concentration, and its intensity was affected by the freshness of eggs, egg variety, and measuring temperature. A nonlinear prediction model, or a detection function, was developed using 182 measurements to predict yolk concentration with a known storage time. This highly sensitive method was validated using 102 separate measurements.A computational analysis framework for molecular cell dynamics: Case-study of exocytosis
AbstractChen, W., Zhou, W., Xia, T., & Gu, X. (n.d.).Publication year
2012Journal title
PloS oneVolume
7Issue
7AbstractOne difficulty in conducting biologically meaningful dynamic analysis at the systems biology level is that in vivo system regulation is complex. Meanwhile, many kinetic rates are unknown, making global system analysis intractable in practice. In this article, we demonstrate a computational pipeline to help solve this problem, using the exocytotic process as an example. Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles that contain a wide range of intracellular molecules. During this process a set of proteins called SNAREs acts as an engine in this vesicle-membrane fusion, by forming four-helical bundle complex between (membrane) target-specific and vesicle-specific SNAREs. As expected, the regulatory network for exocytosis is very complex. Based on the current understanding of the protein-protein interaction network related to exocytosis, we mathematically formulated the whole system, by the ordinary differential equations (ODE). We then applied a mathematical approach (called inverse problem) to estimating the kinetic parameters in the fundamental subsystem (without regulation) from limited in vitro experimental data, which fit well with the reports by the conventional assay. These estimates allowed us to conduct an efficient stability analysis under a specified parameter space for the exocytotic process with or without regulation. Finally, we discuss the potential of this approach to explain experimental observations and to make testable hypotheses for further experimentation.