FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).

SUMMARY: Densely measured SNP data are routinely analyzed but face challenges due to its high dimensionality, especially when gene-environment interactions are incorporated. In recent literature, a functional analysis strategy has been developed, which treats dense SNP measurements as a realization of a genetic function and can 'bypass' the dimensionality challenge. However, there is a lack of portable and friendly software, which hinders practical utilization of these functional methods. We fill this knowledge gap and develop the R package FunctanSNP. This comprehensive package encompasses estimation, identification, and visualization tools and has undergone extensive testing using both simulated and real data, confirming its reliability. FunctanSNP can serve as a convenient and reliable tool for analyzing SNP and other densely measured data. AVAILABILITY AND IMPLEMENTATION: The package is available at https://CRAN.R-project.org/package=FunctanSNP.

Pathological imaging-assisted cancer gene-environment interaction analysis.

Gene-environment (G-E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G-E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the "main effects, interactions" variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G-E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a "byproduct" of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G-E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G-E interaction analysis leads to different findings with competitive prediction performance and stability.

Multivariate functional data clustering using adaptive density peak detection.

Clustering for multivariate functional data is a challenging problem since the data are represented by a set of curves and functions belonging to an infinite-dimensional space. In this article, we propose a novel clustering method for multivariate functional data using an adaptive density peak detection technique. It is a quick cluster center identification algorithm based on the two measures of each functional data observation: the functional density estimate and the distance to the closest observation with a higher functional density. We suggest two types of functional density estimators for multivariate functional data. The first one is a functional k $$ k $$ -nearest neighbor density estimator based on (a) an L2 distance between raw functional curves, or (b) a semimetric of multivariate functional principal components. The second one is a k $$ k $$ -nearest neighbor density estimator based on multivariate functional principal scores. Our clustering method is computationally fast since it does not need an iterative process. The flexibility and advantages of the method are examined by comparing it with other existing clustering methods in simulation studies. A user-friendly R package FADPclust is developed for public use. Finally, our method is applied to a real case study in lung cancer research.

Structured sparse support vector machine with ordered features.

In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ 2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method.

iSFun: an R package for integrative dimension reduction analysis.

SUMMARY: In the analysis of high-dimensional omics data, dimension reduction techniques-including principal component analysis (PCA), partial least squares (PLS) and canonical correlation analysis (CCA)-have been extensively used. When there are multiple datasets generated by independent studies with compatible designs, integrative analysis has been developed and shown to outperform meta-analysis, other multidatasets analysis, and individual-data analysis. To facilitate integrative dimension reduction analysis in daily practice, we develop the R package iSFun, which can comprehensively conduct integrative sparse PCA, PLS and CCA, as well as meta-analysis and stacked analysis. The package can conduct analysis under the homogeneity and heterogeneity models and with the magnitude- and sign-based contrasted penalties. As a 'byproduct', this article is the first to develop integrative analysis built on the CCA technique, further expanding the scope of integrative analysis. AVAILABILITY AND IMPLEMENTATION: The package is available at https://CRAN.R-project.org/package=iSFun. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Biclustering analysis of functionals via penalized fusion.

In biomedical data analysis, clustering is commonly conducted. Biclustering analysis conducts clustering in both the sample and covariate dimensions and can more comprehensively describe data heterogeneity. In most of the existing biclustering analyses, scalar measurements are considered. In this study, motivated by time-course gene expression data and other examples, we take the "natural next step" and consider the biclustering analysis of functionals under which, for each covariate of each sample, a function (to be exact, its values at discrete measurement points) is present. We develop a doubly penalized fusion approach, which includes a smoothness penalty for estimating functionals and, more importantly, a fusion penalty for clustering. Statistical properties are rigorously established, providing the proposed approach a strong ground. We also develop an effective ADMM algorithm and accompanying R code. Numerical analysis, including simulations, comparisons, and the analysis of two time-course gene expression data, demonstrates the practical effectiveness of the proposed approach.

Global burden of acute viral hepatitis and its association with socioeconomic development status, 1990-2019.

BACKGROUND & AIMS: Acute viral hepatitis (AVH) represents an important global health problem; however, the progress in understanding AVH is limited because of the priority of combating persistent HBV and HCV infections. Therefore, an improved understanding of the burden of AVH is required to help design strategies for global intervention. METHODS: Data on 4 major AVH types, including acute hepatitis A, B, C, and E, excluding D, were collected by the Global Burden of Disease (GBD) 2019 database. Age-standardized incidence rates and disability-adjusted life year (DALY) rates for AVH were extracted from GBD 2019 and stratified by sex, level of socio-demographic index (SDI), country, and territory. The association between the burden of AVH and socioeconomic development status, as represented by the SDI, was described. RESULTS: In 2019, there was an age-standardized incidence rate of 3,615.9 (95% CI 3,360.5-3,888.3) and an age-standardized DALY rate of 58.0 (47.3-70.0) per 100,000 person-years for the 4 major types of AVH. Among the major AVH types, acute hepatitis A caused the heaviest burden. There was a significant downward trend in age-standardized DALY rates caused by major incidences of AVH between 1990 and 2019. In 2019, regions or countries located in West and East Africa exhibited the highest age-standardized incidence rates of the 4 major AVH types. These rates were stratified by SDI: high SDI and high-middle SDI locations recorded the lowest incidence and DALY rates of AVH, whereas the low-middle SDI and low SDI locations showed the highest burden of AVH. CONCLUSIONS: The socioeconomic development status and burden of AVH are associated. Therefore, the GBD 2019 data should be used by policymakers to guide cost-effective interventions for AVH. LAY SUMMARY: We identified a negative association between socioeconomic development status and the burden of acute viral hepatitis. The lowest burden of acute viral hepatitis was noted for rich countries, whereas the highest burden of acute viral hepatitis was noted for poor countries.

Public Health Interventions' Effect on Hospital Use in Patients With COVID-19: Comparative Study.

BACKGROUND: Different states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future. OBJECTIVE: We aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions. METHODS: This observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied. RESULTS: The interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio's transmission rates declined before the state's "stay at home" order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again. CONCLUSIONS: The series of public health interventions in three areas were temporally associated with the burden of COVID-19-attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

Smooth and Locally Sparse Estimation for Multiple-Output Functional Linear Regression.

Functional data analysis has attracted substantial research interest and the goal of functional sparsity is to produce a sparse estimate which assigns zero values over regions where the true underlying function is zero, i.e., no relationship between the response variable and the predictor variable. In this paper, we consider a functional linear regression models that explicitly incorporates the interconnections among the responses. We propose a locally sparse (i.e., zero on some subregions) estimator, multiple-smooth and locally sparse (m-SLoS) estimator, for coefficient functions base on the interconnections among the responses. This method is based on a combination of smooth and locally sparse (SLoS) estimator and Laplacian quadratic penalty function, where we used SLoS for encouraging locally sparse and Laplacian quadratic penalty for promoting similar locally sparse among coefficient functions associated with the interconnections among the responses. Simulations show excellent numerical performance of the proposed method in terms of the estimation of coefficient functions especially the coefficient functions are same for all multivariate responses. Practical merit of this modeling is demonstrated by one real application and the prediction shows significant improvements.

Structured Analysis of the High-dimensional FMR Model.

The finite mixture of regression (FMR) model is a popular tool for accommodating data heterogeneity. In the analysis of FMR models with high-dimensional covariates, it is necessary to conduct regularized estimation and identify important covariates rather than noises. In the literature, there has been a lack of attention paid to the differences among important covariates, which can lead to the underlying structure of covariate effects. Specifically, important covariates can be classified into two types: those that behave the same in different subpopulations and those that behave differently. It is of interest to conduct structured analysis to identify such structures, which will enable researchers to better understand covariates and their associations with outcomes. Specifically, the FMR model with high-dimensional covariates is considered. A structured penalization approach is developed for regularized estimation, selection of important variables, and, equally importantly, identification of the underlying covariate effect structure. The proposed approach can be effectively realized, and its statistical properties are rigorously established. Simulation demonstrates its superiority over alternatives. In the analysis of cancer gene expression data, interesting models/structures missed by the existing analysis are identified.

Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers.

This article is motivated by a study of lung cancer prediction using breath volatile organic compound (VOC) biomarkers, where the challenge is that the predictors include not only high-dimensional time-dependent or functional VOC features but also the time-independent clinical variables. We consider a high-dimensional logistic regression and propose two different penalties: group spline-penalty or group smooth-penalty to handle the group structures of the time-dependent variables in the model. The new methods have the advantage for the situation where the model coefficients are sparse but change smoothly within the group, compared with other existing methods such as the group lasso and the group bridge approaches. Our methods are easy to implement since they can be turned into a group minimax concave penalty problem after certain transformations. We show that our fitting algorithm possesses the descent property and leads to attractive convergence properties. The simulation studies and the lung cancer application are performed to demonstrate the accuracy and stability of the proposed approaches.

Integrating approximate single factor graphical models.

In the analysis of complex and high-dimensional data, graphical models have been commonly adopted to describe associations among variables. When common factors exist which make the associations dense, the single factor graphical model has been proposed, which first extracts the common factor and then conducts graphical modeling. Under other simpler contexts, it has been recognized that results generated from analyzing a single dataset are often unsatisfactory, and integrating multiple datasets can effectively improve variable selection and estimation. In graphical modeling, the increased number of parameters makes the "lack of information" problem more severe. In this article, we integrate multiple datasets and conduct the approximate single factor graphical model analysis. A novel penalization approach is developed for the identification and estimation of important loadings and edges. An effective computational algorithm is developed. A wide spectrum of simulations and the analysis of breast cancer gene expression datasets demonstrate the competitive performance of the proposed approach. Overall, this study provides an effective new venue for taking advantage of multiple datasets and improving graphical model analysis.

Assisted graphical model for gene expression data analysis.

The analysis of gene expression data has been playing a pivotal role in recent biomedical research. For gene expression data, network analysis has been shown to be more informative and powerful than individual-gene and geneset-based analysis. Despite promising successes, with the high dimensionality of gene expression data and often low sample sizes, network construction with gene expression data is still often challenged. In recent studies, a prominent trend is to conduct multidimensional profiling, under which data are collected on gene expressions as well as their regulators (copy number variations, methylation, microRNAs, SNPs, etc). With the regulation relationship, regulators contain information on gene expressions and can potentially assist in estimating their characteristics. In this study, we develop an assisted graphical model (AGM) approach, which can effectively use information in regulators to improve the estimation of gene expression graphical structure. The proposed approach has an intuitive formulation and can adaptively accommodate different regulator scenarios. Its consistency properties are rigorously established. Extensive simulations and the analysis of a breast cancer gene expression data set demonstrate the practical effectiveness of the AGM.

Integrative sparse principal component analysis of gene expression data.

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance.

Promoting structural effects of covariates in the cure rate model with penalization.

Cure rate models have been widely adopted for characterizing survival data that have long-term survivors. Under a mixture cure rate model where the population is a mixture of cured and susceptible subjects, a primary goal is to study covariate effects on the cure probability and survival function of the susceptible subjects. In this article, we propose a penalization method for estimating the mixture cure rate model where we explicitly consider the structural effects of covariates. The proposed method is more informative than the standard estimations and more flexible than the existing works on structural effects. Depending on data characteristics, we develop different penalties and corresponding computational algorithms. Simulation shows that the proposed method outperforms the alternatives by more accurately estimating parameters and identifying relevant variables. Two breast cancer datasets, one with low-dimensional clinical variables and the other with high-dimensional genetic variables, are analyzed.

Noninvasive scoring system for significant inflammation related to chronic hepatitis B.

Although a liver stiffness measurement-based model can precisely predict significant intrahepatic inflammation, transient elastography is not commonly available in a primary care center. Additionally, high body mass index and bilirubinemia have notable effects on the accuracy of transient elastography. The present study aimed to create a noninvasive scoring system for the prediction of intrahepatic inflammatory activity related to chronic hepatitis B, without the aid of transient elastography. A total of 396 patients with chronic hepatitis B were enrolled in the present study. Liver biopsies were performed, liver histology was scored using the Scheuer scoring system, and serum markers and liver function were investigated. Inflammatory activity scoring models were constructed for both hepatitis B envelope antigen (+) and hepatitis B envelope antigen (-) patients. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 86.00%, 84.80%, 62.32%, 95.39%, and 0.9219, respectively, in the hepatitis B envelope antigen (+) group and 91.89%, 89.86%, 70.83%, 97.64%, and 0.9691, respectively, in the hepatitis B envelope antigen (-) group. Significant inflammation related to chronic hepatitis B can be predicted with satisfactory accuracy by using our logistic regression-based scoring system.

Analyzing large datasets with bootstrap penalization.

Data with a large p (number of covariates) and/or a large n (sample size) are now commonly encountered. For many problems, regularization especially penalization is adopted for estimation and variable selection. The straightforward application of penalization to large datasets demands a "big computer" with high computational power. To improve computational feasibility, we develop bootstrap penalization, which dissects a big penalized estimation into a set of small ones, which can be executed in a highly parallel manner and each only demands a "small computer". The proposed approach takes different strategies for data with different characteristics. For data with a large p but a small to moderate n, covariates are first clustered into relatively homogeneous blocks. The proposed approach consists of two sequential steps. In each step and for each bootstrap sample, we select blocks of covariates and run penalization. The results from multiple bootstrap samples are pooled to generate the final estimate. For data with a large n but a small to moderate p, we bootstrap a small number of subjects, apply penalized estimation, and then conduct a weighted average over multiple bootstrap samples. For data with a large p and a large n, the natural marriage of the previous two methods is applied. Numerical studies, including simulations and data analysis, show that the proposed approach has computational and numerical advantages over the straightforward application of penalization. An R package has been developed to implement the proposed methods.

Enhanced HBsAg synthesis correlates with increased severity of fibrosis in chronic hepatitis B patients.

BACKGROUND AND AIMS: Little is known about whether low serum HBsAg levels result from impaired HBsAg synthesis or a reduced number of hepatocytes caused by advanced liver fibrosis. Therefore, we investigated the capacity for HBsAg synthesis in a cross-sectional cohort of treatment-naïve chronic hepatitis B patients. METHODS: Chronic hepatitis B patients (n = 362) were enrolled; liver biopsies were performed and liver histology was scored, and serum HBsAg and HBV DNA levels were investigated. In the enrolled patients, 183 out of 362 have quantitative serum HBsAg levels. Tissue HBsAg was determined by immunohistochemistry. RESULTS: A positive correlation between serum HBsAg and HBV DNA levels was revealed in HBeAg(+) patients (r = 0.2613, p = 0.0050). In HBeAg(+) patients, serum HBsAg and severity of fibrosis were inversely correlated (p = 0.0094), whereas tissue HBsAg levels correlated positively with the stage of fibrosis (p = 0.0280). After applying the mean aminopyrine breath test as a correction factor, adjusted serum HBsAg showed a strong positive correlation with fibrosis severity in HBeAg(+) patients (r = 0.5655, p<0.0001). The adjusted serum HBsAg values predicted 'moderate to severe' fibrosis with nearly perfect performance in both HBeAg(+) patients (area under the curve: 0.994, 95% CI: 0.983-1.000) and HBeAg(-) patients (area under the curve: 1.000, 95% CI: 1.000-1.000). CONCLUSIONS: Although serum HBsAg levels were negatively correlated with fibrosis severity in HBeAg(+) patients, aminopyrine breath test-adjusted serum HBsAg and tissue HBsAg, two indices that are unaffected by the number of residual hepatocytes, were positively correlated with fibrosis severity. Furthermore, adjusted serum HBsAg has an accurate prediction capability.

Illness, medical expenditure and household consumption: observations from Taiwan.

BACKGROUND: Illness conditions lead to medical expenditure. Even with various types of medical insurance, there can still be considerable out-of-pocket costs. Medical expenditure can affect other categories of household consumptions. The goal of this study is to provide an updated empirical description of the distributions of illness conditions and medical expenditure and their associations with other categories of household consumptions. METHODS: A phone-call survey was conducted in June and July of 2012. The study was approved by ethics review committees at Xiamen University and FuJen Catholic University. Data was collected using a Computer-Assisted Telephone Survey System (CATSS). "Household" was the unit for data collection and analysis. Univariate and multivariate analyses were conducted, examining the distributions of illness conditions and the associations of illness and medical expenditure with other household consumptions. RESULTS: The presence of chronic disease and inpatient treatment was not significantly associated with household characteristics. The level of per capita medical expenditure was significantly associated with household size, income, and household head occupation. The presence of chronic disease was significantly associated with levels of education, insurance and durable goods consumption. After adjusting for confounders, the associations with education and durable goods consumption remained significant. The presence of inpatient treatment was not associated with consumption levels. In the univariate analysis, medical expenditure was significantly associated with all other consumption categories. After adjusting for confounding effects, the associations between medical expenditure and the actual amount of entertainment expenses and percentages of basic consumption, savings, and insurance (as of total consumption) remained significant. CONCLUSION: This study provided an updated description of the distributions of illness conditions and medical expenditure in Taiwan. The findings were mostly positive in that illness and medical expenditure were not observed to be significantly associated with other consumption categories. This observation differed from those made in some other Asian countries and could be explained by the higher economic status and universal basic health insurance coverage of Taiwan.

Health insurance coverage, medical expenditure and coping strategy: evidence from Taiwan.

BACKGROUND: The health insurance system in Taiwan is comprised of public health insurance and private health insurance. The public health insurance, called "universal national health insurance" (NHI), was first established in 1995 and amended in 2011. The goal of this study is to provide an updated description of several important aspects of health insurance in Taiwan. Of special interest are household insurance coverage, medical expenditures (both gross and out-of-pocket), and coping strategies. METHODS: Data was collected via a phone call survey conducted in August and September of 2011. A household was the unit for survey and data analysis. A total of 2,424 households covering all major counties and cities in Taiwan were surveyed. RESULTS: The survey revealed that households with smaller sizes and higher incomes were more likely to have higher coverage of public and private health insurance. In addition, households with the presence of chronic diseases were more likely to have both types of insurance. Analysis of both gross and out-of-pocket medical expenditure was conducted. It was suggested that health insurance could not fully remove the financial burden caused by illness. The presence of chronic disease and inpatient treatment were significantly associated with higher gross and out-of-pocket medical expenditure. In addition, the presence of inpatient treatment was significantly associated with extremely high medical expenditure. Regional differences were also observed, with households in the northern, central, and southern regions having less gross medical expenditures than those on the offshore islands. Households with the presence of inpatient treatment were more likely to cope with medical expenditure using means other than salaries. CONCLUSION: Despite the considerable achievements of the health insurance system in Taiwan, there is still room for improvement. This study investigated coverage, cost, and coping strategies and may be informative to stakeholders of both basic and commercial health insurance.