Adaptive Bayesian variable clustering via structural learning of breast cancer data.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

Optimization of EWOC principle in BLRM design for phase 1 oncology trials.

Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice.

Variable selection in nonlinear function-on-scalar regression.

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003-2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.

Gate-Controlled Chiral Recognition and Spin Assessment with All-Electric Hybrid Quantum Wire-Based Transistors.

The search for inexpensive and all-electric tunable methods for portable and fast recognition and discrimination between various chiral enantiomers, mainly those found in the gas phase, has been one of the most challenging tasks in the field of analytical chemistry. The current article reports on a chiral sensitive electric architecture (CSEA) of a helical polyaniline (PANI)@carbon nanotube (CNT) hybrid quantum-wire based field effect transistor (FET) platform. The CSEA architecture exhibits gate-controlled-channel-chirality modulation for the selective distinction of Limonene (S(+)/R(-)) at ≈12 V intervals. Typical gate-modulated selectivity of S(+)-Limonene and R(-)-Limonene using two opposite helically turned hybrids, namely as, S-PANI@CNT and R-PANI@CNT are 6.5 and 2.8, respectively. Theoretical analysis and modelling relates the gas-phase chiral quantum probe with spin-channel modulation in CNT by Rashba spin-orbit interaction. This is achieved by applied gate voltage, CNT's unique curved surface, adsorbed chiral adatom induced scattering center on the curved graphitic lattice and helicoid field from a synthetically prepared helical PANI@CNT hybrid interface.

Robust kernel association testing (RobKAT).

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

Bayesian structural equation modeling in multiple omics data with application to circadian genes.

MOTIVATION: It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. RESULTS: Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. AVAILABILITY AND IMPLEMENTATION: The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.

BACKGROUND: The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together. Our goal was to determine if survival outcomes are associated with circadian clock function. To accomplish this goal, we developed a Bayesian hierarchical survival model coupled with the global local shrinkage prior and applied this model to available RNASeq and Copy Number Variation data to select significant circadian genes associates with cancer progression. RESULTS: Using a Bayesian shrinkage approach with the Bayesian accelerated failure time (AFT) model we showed the circadian clock associated gene DEC1 is positively correlated to survival outcome in breast cancer patients. The R package circgene implementing the methodology is available at https://github.com/MAITYA02/circgene. CONCLUSIONS: The proposed Bayesian hierarchical model is the first shrinkage prior based model in its kind which integrates two omics platforms to identify the significant circadian gene for cancer survival.

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.

In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models. The model parameters are estimated by the method of maximum likelihood. The finite-sample properties of the estimators are studied using Monte Carlo simulations. The empirical study suggests that the degree of association among the outcome processes influences the bias, efficiency, and coverage probability of the estimators. Our proposed joint model estimators are approximately unbiased and produce smaller mean squared errors as compared to the estimators obtained from separate models. This work is motivated by a large multicenter study, referred to as the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. We apply our proposed method to the GenIMS data analysis.

A Dinickel Catalyzed Cyclopropanation without the Formation of a Metal Carbene Intermediate.

(NDI)Ni2 catalysts (NDI=naphthyridine-diimine) promote cyclopropanation reactions of 1,3-dienes using (Me3 Si)CHN2 . Mechanistic studies reveal that a metal carbene intermediate is not part of the catalytic cycle. The (NDI)Ni2 (CHSiMe3 ) complex was independently synthesized and found to be unreactive toward dienes. Based on DFT models, we propose an alternative mechanism that begins with a Ni2 -mediated coupling of (Me3 Si)CHN2 and the diene. N2 extrusion followed by radical C-C bond formation generates the cyclopropane product. This model reproduces the experimentally observed regioselectivity and diastereoselectivity of the reaction.

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.

Inference on phenotype-specific effects of genes using multivariate kernel machine regression.

We consider the problem of assessing the joint effect of a set of genetic markers on multiple, possibly correlated phenotypes of interest. We develop a kernel machine based multivariate regression framework, where the joint effect of the marker set on each of the phenotypes is modeled using prespecified kernel functions with unknown variance components. Unlike most existing methods that mainly focus on the global association between the marker set and the phenotype set, we develop estimation and testing procedures to study phenotype-specific associations. Specifically, we develop an estimation method based on the penalized likelihood approach to estimate phenotype-specific effects and their corresponding standard errors while accounting for possible correlation among the phenotypes. We develop testing procedures for the association of the marker set with any subset of phenotypes using a score-based variance components testing method. We assess the performance of our proposed methodology via a simulation study and demonstrate the utility of the proposed method using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) data.

On the substructure controls in rare variant analysis: Principal components or variance components?

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene-environment interaction effect.

Most common human diseases are a result from the combined effect of genes, the environmental factors, and their interactions such that including gene-environment (GE) interactions can improve power in gene mapping studies. The standard strategy is to test the SNPs, one-by-one, using a regression model that includes both the SNP effect and the GE interaction. However, the SNP-by-SNP approach has serious limitations, such as the inability to model epistatic SNP effects, biased estimation, and reduced power. Thus, in this article, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function andwe model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. Extensive simulations and real data analysis show that our proposed method has correct type I error and can have higher power than score-based approaches under many situations.

A small-sample multivariate kernel machine test for microbiome association studies.

High-throughput sequencing technologies have enabled large-scale studies of the role of the human microbiome in health conditions and diseases. Microbial community level association test, as a critical step to establish the connection between overall microbiome composition and an outcome of interest, has now been routinely performed in many studies. However, current microbiome association tests all focus on a single outcome. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery. As these outcomes may share common mechanisms, jointly analyzing these outcomes can amplify the association signal and improve statistical power to detect potential associations. We propose the multivariate microbiome regression-based kernel association test (MMiRKAT) for testing association between multiple continuous outcomes and overall microbiome composition, where the kernel used in MMiRKAT is based on Bray-Curtis or UniFrac distance. MMiRKAT directly regresses all outcomes on the microbiome profiles via a semiparametric kernel machine regression framework, which allows for covariate adjustment and evaluates the association via a variance-component score test. Because most of the current microbiome studies have small sample sizes, a novel small-sample correction procedure is implemented in MMiRKAT to correct for the conservativeness of the association test when the sample size is small or moderate. The proposed method is assessed via simulation studies and an application to a real data set examining the association between host gene expression and mucosal microbiome composition. We demonstrate that MMiRKAT is more powerful than large sample based multivariate kernel association test, while controlling the type I error. A free implementation of MMiRKAT in R language is available at http://research.fhcrc.org/wu/en.html.

Resonance-Frequency Modulation for Rapid, Point-of-Care Ebola-Glycoprotein Diagnosis with a Graphene-Based Field-Effect Biotransistor.

Recent outbreaks of Ebola-virus infections in several countries demand a rapid point-of-care (POC)-detection strategy. This paper reports on an innovative pathway founded on electronic-resonance-frequency modulation to detect Ebola glycoprotein (GP), on the basis of a carrier-injection-trapping-release-transfer mechanism and the standard antibody-antigen-interaction principle within a dielectric-gated reduced graphene oxide (rGO) field-effect transistor (GFET). The sensitivity of Ebola detection can be significantly enhanced by monitoring the device's electronic-resonance frequency, such as its inflection frequency ( fi), where the phase angle reaches a maximum (θmax). In addition to excellent selectivity, a sensitivity of ∼36-160% and ∼17-40% for 0.001-3.401 mg/L Ebola GP can be achieved at high and low inflection-resonance frequencies, respectively, which are several orders of magnitude higher than the sensitivity from other electronic parameters (e.g., resistance-based sensitivity). Using equivalent circuit modeling for contributions from channel and contact, analytical equations for resonance shifts have been generalized. When matching with the incoming ac-measurement signal, electronic resonance from the phase-angle spectrum evolves from various relaxation processes (e.g., trap and release of injected charges at surface-trap sites of the channel-gate oxide and channel-source or drain interfaces) that are associated with a characteristic emission frequency. Using charge-relaxation dynamics, a high-performance bio-FET sensing platform for healthcare and bioelectronic applications is realized through resonance shifting.

Functional interaction-based nonlinear models with application to multiplatform genomics data.

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.

Impedimetric phosphorene field-effect transistors for rapid detection of lead ions.

Stimuli-responsive field-effect transistors (FETs) based on 2D nanomaterials have been considered as attractive candidates for sensing applications due to their rapid response, high sensitivity, and real-time monitoring capabilities. Here we report on an impedance spectroscopy technique for FET sensor applications with ultra-high sensitivity and good reproducibility. An alumina-gated FET, using an ultra-thin black phosphorus flake as the channel material, shows significantly improved stability and ultra-high sensitivity to lead ions in water. In addition, the phase angle in the low frequency region was found to change significantly in the presence of lead ion solutions, whereas it was almost unchanged in the high frequency region. The dominant sensing performance was found at low frequency phase spectrum around 50 Hz and a systematic change in the phase angle in different lead ion concentrations was found. Applying the impedance spectroscopy technique to insulator-gated FET sensors could open a new avenue for real-world sensor applications.

A Terminal Iron(IV) Nitride Supported by a Super Bulky Guanidinate Ligand and Examination of Its Electronic Structure and Reactivity.

Utilizing the bulky guanidinate ligand [LAr*]- (LAr* = (Ar*N)2C(R), Ar* = 2,6-bis(diphenylmethyl)-4-tert-butylphenyl, R = NCtBu2) for kinetic stabilization, the synthesis of a rare terminal Fe(IV) nitride complex is reported. UV irradiation of a pyridine solution of the Fe(II) azide [LAr*]FeN3(py) (3-py) at 0 °C cleanly generates the Fe(IV) nitride [LAr*]FeN(py) (1). The 15N NMR spectrum of the 115N (50% Fe≡15N) isotopomer shows a resonance at 1016 ppm (vs externally referenced CH3NO2 at 380 ppm), comparable to that known for other terminal iron nitrides. Notably, the computed structure of 1 reveals an iron center with distorted tetrahedral geometry, τ4 = 0.72, featuring a short Fe≡N bond (1.52 Å). Inspection of the frontier orbital ordering of 1 shows a relatively small HOMO/LUMO gap with the LUMO comprised by Fe(dxz,yz)N(px,y) π*-orbitals, a splitting that is manifested in the electronic absorption spectrum of 1 (λ = 610 nm, ε = 1375 L·mol-1·cm-1; λ = 613 nm (calcd)). Complex 1 persists in low-temperature solutions of pyridine but becomes unstable at room temperature, gradually converting to the Fe(II) hydrazide product [κ2-(tBu2CN)C(η6-NAr*)(N-NAr*)]Fe (4) upon standing via intramolecular N-atom insertion. This reactivity of the Fe≡N moiety was assessed through molecular orbital analysis, which suggests electrophilic character at the nitride functionality. Accordingly, treatment of 1 with the nucleophiles PMe2Ph and Ar-N≡C (Ar = 2,6-dimethylphenyl) leads to partial N-atom transfer and formation of the Fe(II) addition products [LAr*]Fe(N═PMe2Ph)(py) (5) and [LAr*]Fe(N═C═NAr)(py) (6). Similarly, 1 reacts with PhSiH3 to give [LAr*]Fe[N(H)(SiH2Ph)](py) (7) which Fukui analysis shows to proceed via electrophilic insertion of the nitride into the Si-H bond.

Maternal blood cadmium, lead and arsenic levels, nutrient combinations, and offspring birthweight.

BACKGROUND: Cadmium (Cd), lead (Pb) and arsenic (As) are common environmental contaminants that have been associated with lower birthweight. Although some essential metals may mitigate exposure, data are inconsistent. This study sought to evaluate the relationship between toxic metals, nutrient combinations and birthweight among 275 mother-child pairs. METHODS: Non-essential metals, Cd, Pb, As, and essential metals, iron (Fe), zinc (Zn), selenium (Se), copper (Cu), calcium (Ca), magnesium (Mg), and manganese (Mn) were measured in maternal whole blood obtained during the first trimester using inductively coupled plasma mass spectrometry. Folate concentrations were measured by microbial assay. Birthweight was obtained from medical records. We used quantile regression to evaluate the association between toxic metals and nutrients due to their underlying wedge-shaped relationship. Ordinary linear regression was used to evaluate associations between birth weight and toxic metals. RESULTS: After multivariate adjustment, the negative association between Pb or Cd and a combination of Fe, Se, Ca and folate was robust, persistent and dose-dependent (p < 0.05). However, a combination of Zn, Cu, Mn and Mg was positively associated with Pb and Cd levels. While prenatal blood Cd and Pb were also associated with lower birthweight. Fe, Se, Ca and folate did not modify these associations. CONCLUSION: Small sample size and cross-sectional design notwithstanding, the robust and persistent negative associations between some, but not all, nutrient combinations with these ubiquitous environmental contaminants suggest that only some recommended nutrient combinations may mitigate toxic metal exposure in chronically exposed populations. Larger longitudinal studies are required to confirm these findings.

Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.

Studying complex diseases in the post genome-wide association studies (GWAS) era has led to developing methods that consider factor-sets rather than individual genetic/environmental factors (i.e., Multi-G-Multi-E studies), and mining for potential gene-environment (G×E) interactions has proven to be an invaluable aid in both discovery and deciphering underlying biological mechanisms. Current approaches for examining effect profiles in Multi-G-Multi-E analyses are either underpowered due to large degrees of freedom, ill-suited for detecting G×E interactions due to imprecise modeling of the G and E effects, or lack of capacity for modeling interactions between two factor-sets (e.g., existing methods focus primarily on a single E factor). In this work, we illustrate the issues encountered in constructing kernels for investigating interactions between two factor-sets, and propose a simple yet intuitive solution to construct the G×E kernel that retains the ease-of-interpretation of classic regression. We also construct a series of kernel machine (KM) score tests to evaluate the complete effect profile (i.e., the G, E, and G×E effects individually or in combination). We show, via simulations and a data application, that the proposed KM methods outperform the classic and PC regressions across a range of scenarios, including varying effect size, effect structure, and interaction complexity. The largest power gain was observed when the underlying effect structure involved complex G×E interactions; however, the proposed methods have consistent, powerful performance when the effect profile is simple or complex, suggesting that the proposed method could be a useful tool for exploratory or confirmatory G×E analysis.