Sparse ordinal discriminant analysis.

Ordinal class labels are frequently observed in classification studies across various fields. In medical science, patients' responses to a drug can be arranged in the natural order, reflecting their recovery postdrug administration. The severity of the disease is often recorded using an ordinal scale, such as cancer grades or tumor stages. We propose a method based on the linear discriminant analysis (LDA) that generates a sparse, low-dimensional discriminant subspace reflecting the class orders. Unlike existing approaches that focus on predictors marginally associated with ordinal labels, our proposed method selects variables that collectively contribute to the ordinal labels. We employ the optimal scoring approach for LDA as a regularization framework, applying an ordinality penalty to the optimal scores and a sparsity penalty to the coefficients for the predictors. We demonstrate the effectiveness of our approach using a glioma dataset, where we predict cancer grades based on gene expression. A simulation study with various settings validates the competitiveness of our classification performance and demonstrates the advantages of our approach in terms of the interpretability of the estimated classifier with respect to the ordinal class labels.

Interpretable classification for multivariate gait analysis of cerebral palsy.

BACKGROUND: The Gross Motor Function Classification System (GMFCS) is a widely used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients into different levels based on their gross motor function and its level is typically determined through visual evaluation by a trained expert. Although gait analysis is commonly used in CP research, the functional aspects of gait patterns has yet to be fully exploited. By utilizing the gait patterns to predict GMFCS, we can gain a more comprehensive understanding of how CP affects mobility and develop more effective interventions for CP patients. RESULT: In this study, we propose a multivariate functional classification method to examine the relationship between kinematic gait measures and GMFCS levels in both normal individuals and CP patients with varying GMFCS levels. A sparse linear functional discrimination framework is utilized to achieve an interpretable prediction model. The method is generalized to handle multivariate functional data and multi-class classification. Our method offers competitive or improved prediction accuracy compared to state-of-the-art functional classification approaches and provides interpretable discriminant functions that can characterize the kinesiological progression of gait corresponding to higher GMFCS levels. CONCLUSION: We generalize the sparse functional linear discrimination framework to achieve interpretable classification of GMFCS levels using kinematic gait measures. The findings of this research will aid clinicians in diagnosing CP and assigning appropriate GMFCS levels in a more consistent, systematic, and scientifically supported manner.

Tanshinone IIA-loaded nanoparticles and neural stem cell combination therapy improves gut homeostasis and recovery in a pig ischemic stroke model.

Impaired gut homeostasis is associated with stroke often presenting with leaky gut syndrome and increased gut, brain, and systemic inflammation that further exacerbates brain damage. We previously reported that intracisternal administration of Tanshinone IIA-loaded nanoparticles (Tan IIA-NPs) and transplantation of induced pluripotent stem cell-derived neural stem cells (iNSCs) led to enhanced neuroprotective and regenerative activity and improved recovery in a pig stroke model. We hypothesized that Tan IIA-NP + iNSC combination therapy-mediated stroke recovery may also have an impact on gut inflammation and integrity in the stroke pigs. Ischemic stroke was induced, and male Yucatan pigs received PBS + PBS (Control, n = 6) or Tan IIA-NP + iNSC (Treatment, n = 6) treatment. The Tan IIA-NP + iNSC treatment reduced expression of jejunal TNF-α, TNF-α receptor1, and phosphorylated IkBα while increasing the expression of jejunal occludin, claudin1, and ZO-1 at 12 weeks post-treatment (PT). Treated pigs had higher fecal short-chain fatty acid (SCFAs) levels than their counterparts throughout the study period, and fecal SCFAs levels were negatively correlated with jejunal inflammation. Interestingly, fecal SCFAs levels were also negatively correlated with brain lesion volume and midline shift at 12 weeks PT. Collectively, the anti-inflammatory and neuroregenerative treatment resulted in increased SCFAs levels, tight junction protein expression, and decreased inflammation in the gut.

Resampling-based inferences for compositional regression with application to beef cattle microbiomes.

Gut microbiomes are increasingly found to be associated with many health-related characteristics of humans as well as animals. Regression with compositional microbiomes covariates is commonly used to identify important bacterial taxa that are related to various phenotype responses. Often the dimension of microbiome taxa easily exceeds the number of available samples, which creates a serious challenge in the estimation and inference of the model. The sparse log-contrast regression method is useful for such cases as it can yield a model estimate that depends on only a small number of taxa. However, a formal statistical inference procedure for individual regression coefficients has not been properly established yet. We propose a new estimation and inference procedure for linear regression models with extremely low-sample-sized compositional predictors. Under the compositional log-contrast regression framework, the proposed approach consists of two steps. The first step is to screen relevant predictors by fitting a log-contrast model with a sparse penalty. The screened-in variables are used as predictors in the non-sparse log-contrast model in the second step, where each of the regression coefficients is tested using nonparametric, resampling-based methods such as permutation and bootstrap. The performances of the proposed methods are evaluated by a simulation study, which shows they outperform traditional approaches based on normal assumptions or large sample asymptotics. Application to steer microbiome data successfully identifies key bacterial taxa that are related to important cattle quality measures.

Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association.

Many research proposals involve collecting multiple sources of information from a set of common samples, with the goal of performing an integrative analysis describing the associations between sources. We propose a method that characterizes the dominant modes of co-variation between the variables in two datasets while simultaneously performing variable selection. Our method relies on a sparse, low rank approximation of a matrix containing pairwise measures of association between the two sets of variables. We show that the proposed method shares a close connection with another group of methods for integrative data analysis - sparse canonical correlation analysis (CCA). Under some assumptions, the proposed method and sparse CCA aim to select the same subsets of variables. We show through simulation that the proposed method can achieve better variable selection accuracies than two state-of-the-art sparse CCA algorithms. Empirically, we demonstrate through the analysis of DNA methylation and gene expression data that the proposed method selects variables that have as high or higher canonical correlation than the variables selected by sparse CCA methods, which is a rather surprising finding given that objective function of the proposed method does not actually maximize the canonical correlation.

Beyond Depth and Breadth: Taking "Types" of Health Information Sought into Consideration with Cluster Analysis.

Online health information-seeking behavior (OHIS) has been typically operationalized in an aggregate form representing either depth (e.g., how long) or breadth (e.g., how much) of seeking, which is irrespective of what types of information are sought. Recognizing limitations of such practice, this research employs cluster analysis to reflect the content and types of health information sought in studying OHIS. Three online studies providing participants with opportunities to actually seek information about meningitis (Study 1; N = 408), Alzheimer's disease (Study 2; N = 190), and cancer (Study 3; N = 208) recorded the participants' information-seeking activities unobtrusively. Across the three studies, cluster analysis identified three common clusters representing distinctive information-seeking patterns (i.e., combinations of different types of information sought): One cluster sought information on "overview," the second one focused on "protection" information, and the third cluster sought "all" types of information provided. The relative preference for these types of information was predicted by several antecedents of information-seeking behavior proposed in Comprehensive Model of Information Seeking (CMIS) including age, fear, self- and response-efficacy. The findings demonstrate the utility of taking the actual content or types of health information sought into consideration and suggest several fruitful avenues it paves for future research on OHIS.

Feature-weighted ordinal classification for predicting drug response in multiple myeloma.

MOTIVATION: Ordinal classification problems arise in a variety of real-world applications, in which samples need to be classified into categories with a natural ordering. An example of classifying high-dimensional ordinal data is to use gene expressions to predict the ordinal drug response, which has been increasingly studied in pharmacogenetics. Classical ordinal classification methods are typically not able to tackle high-dimensional data and standard high-dimensional classification methods discard the ordering information among the classes. Existing work of high-dimensional ordinal classification approaches usually assume a linear ordinality among the classes. We argue that manually labeled ordinal classes may not be linearly arranged in the data space, especially in high-dimensional complex problems. RESULTS: We propose a new approach that can project high-dimensional data into a lower discriminating subspace, where the innate ordinal structure of the classes is uncovered. The proposed method weights the features based on their rank correlations with the class labels and incorporates the weights into the framework of linear discriminant analysis. We apply the method to predict the response to two types of drugs for patients with multiple myeloma, respectively. A comparative analysis with both ordinal and nominal existing methods demonstrates that the proposed method can achieve a competitive predictive performance while honoring the intrinsic ordinal structure of the classes. We provide interpretations on the genes that are selected by the proposed approach to understand their drug-specific response mechanisms. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the Gene Expression Omnibus Database at https://www.ncbi.nlm.nih.gov/geo/ and can be accessed with accession number GSE9782 and GSE68871. The source code for FWOC can be accessed at https://github.com/pisuduo/Feature-Weighted-Ordinal-Classification-FWOC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model.

Magnetic resonance imaging (MRI) is a clinically relevant, real-time imaging modality that is frequently utilized to assess stroke type and severity. However, specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need. Consequently, the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke. Stroke was induced via permanent middle cerebral artery occlusion. At 24 hours post-stroke, MRI analysis revealed focal ischemic lesions, decreased diffusivity, hemispheric swelling, and white matter degradation. Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke. Gaussian graphical models identified specific MRI outputs and functional recovery variables, including white matter integrity and gait performance, that exhibited strong conditional dependencies. Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance. Consequently, these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities (e.g., white matter composition) that have proven to be critical in ischemic stroke pathophysiology. The study was approved by the University of Georgia (UGA) Institutional Animal Care and Use Committee (IACUC; Protocol Number: A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5) on November 22, 2017.

Perinatal Docosahexaenoic Acid Supplementation Improves Cognition and Alters Brain Functional Organization in Piglets.

Epidemiologic studies associate maternal docosahexaenoic acid (DHA)/DHA-containing seafood intake with enhanced cognitive development; although, it should be noted that interventional trials show inconsistent findings. We examined perinatal DHA supplementation on cognitive performance, brain anatomical and functional organization, and the brain monoamine neurotransmitter status of offspring using a piglet model. Sows were fed a control (CON) or a diet containing DHA (DHA) from late gestation throughout lactation. Piglets underwent an open field test (OFT), an object recognition test (ORT), and magnetic resonance imaging (MRI) to acquire anatomical, diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI) at weaning. Piglets from DHA-fed sows spent 95% more time sniffing the walls than CON in OFT and exhibited an elevated interest in the novel object in ORT, while CON piglets demonstrated no preference. Maternal DHA supplementation increased fiber length and tended to increase fractional anisotropy in the hippocampus of offspring than CON. DHA piglets exhibited increased functional connectivity in the cerebellar, visual, and default mode network and decreased activity in executive control and sensorimotor network compared to CON. The brain monoamine neurotransmitter levels did not differ in healthy offspring. Perinatal DHA supplementation may increase exploratory behaviors, improve recognition memory, enhance fiber tract integrity, and alter brain functional organization in offspring at weaning.

Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data.

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Simulation studies suggest that the proposed method performs competitive in comparison with some existing methods in identifying true signals in various underlying covariance structures.

Clustering multivariate functional data with phase variation.

When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.

Covariance adjustment for batch effect in gene expression data.

Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.

A multiscale analysis of the temporal characteristics of resting-state fMRI data.

In this paper, we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) time series using multiscale analysis tools, SiZer (significance of zero crossings of the derivative) and wavelets. Most current approaches to the analysis of fMRI data assume simple models for temporal (short term or long term) dependence structure. Such simplifications are to some extent necessary due to the complex, high-dimensional nature of the data, but to date there have been few systematic studies of the dependence structures under a range of possible null hypotheses, using data sets gathered specifically for that purpose. We aim to address some of these issues by analyzing the detrended data with a long enough time horizon to study possible long-range temporal dependence. Our multiscale approach shows that even for resting-state data, data, i.e. "null" or ambient thought, some voxel time series cannot be modeled by white noise and need long-range dependent type error structure. This finding suggests the use of different time series models in different parts of the brain in fMRI studies.

Gene selection using support vector machines with non-convex penalty.

MOTIVATION: With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes simultaneously in one single experiment. One current difficulty in interpreting microarray data comes from their innate nature of 'high-dimensional low sample size'. Therefore, robust and accurate gene selection methods are required to identify differentially expressed group of genes across different samples, e.g. between cancerous and normal cells. Successful gene selection will help to classify different cancer types, lead to a better understanding of genetic signatures in cancers and improve treatment strategies. Although gene selection and cancer classification are two closely related problems, most existing approaches handle them separately by selecting genes prior to classification. We provide a unified procedure for simultaneous gene selection and cancer classification, achieving high accuracy in both aspects. RESULTS: In this paper we develop a novel type of regularization in support vector machines (SVMs) to identify important genes for cancer classification. A special nonconvex penalty, called the smoothly clipped absolute deviation penalty, is imposed on the hinge loss function in the SVM. By systematically thresholding small estimates to zeros, the new procedure eliminates redundant genes automatically and yields a compact and accurate classifier. A successive quadratic algorithm is proposed to convert the non-differentiable and non-convex optimization problem into easily solved linear equation systems. The method is applied to two real datasets and has produced very promising results. AVAILABILITY: MATLAB codes are available upon request from the authors.

Blood transfusion is an independent predictor of increased mortality in nonoperatively managed blunt hepatic and splenic injuries.

BACKGROUND: Management strategies for blunt solid viscus injuries often include blood transfusion. However, transfusion has previously been identified as an independent predictor of mortality in unselected trauma admissions. We hypothesized that transfusion would adversely affect mortality and outcome in patients presenting with blunt hepatic and splenic injuries after controlling for injury severity and degree of shock. METHODS: We retrospectively reviewed records from all adults with blunt hepatic and/or splenic injuries admitted to a Level I trauma center over a 4-year period. Demographics, physiologic variables, injury severity, and amount of blood transfused were analyzed. Univariate and multivariate analysis with logistic and linear regression were used to identify predictors of mortality and outcome. RESULTS: One hundred forty-three (45%) of 316 patients presenting with blunt hepatic and/or splenic injuries received blood transfusion within the first 24 hours. Two hundred thirty patients (72.8%) were selected for nonoperative management, of whom 75 (33%) required transfusion in the first 24 hours. Transfusion was an independent predictor of mortality in all patients (odds ratio [OR], 4.75; 95% confidence interval [CI], 1.37-16.4; p = 0.014) and in those managed nonoperatively (OR, 8.45; 95% CI, 1.95-36.53; p = 0.0043) after controlling for indices of shock and injury severity. The risk of death increased with each unit of packed red blood cells transfused (OR per unit, 1.16; 95% CI, 1.10-1.24; p < 0.0001). Blood transfusion was also an independent predictor of increased hospital length of stay (coefficient, 5.45; 95% CI, 1.64-9.25; p = 0.005). CONCLUSION: Blood transfusion is a strong independent predictor of mortality and hospital length of stay in patients with blunt liver and spleen injuries after controlling for indices of shock and injury severity. Transfusion-associated mortality risk was highest in the subset of patients managed nonoperatively. Prospective examination of transfusion practices in treatment algorithms of blunt hepatic and splenic injuries is warranted.