precision-implementation-minimal-erythema-dose-med-testing-to-assess

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response

Minimal erythema dose (MED) testing is frequently used in clinical settings for determining the smallest amount of ultraviolet (UV) irradiation necessary ...

Virginia Tech

Functional additive models (FAMs) provide a flexible yet simple framework for regressions involving functional predictors. The utilization of data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting nonlinear additive components has been less studied. In this work, we propose a new regularization framework for the structure estimation in the context of Reproducing Kernel Hilbert Spaces. The proposed approach takes advantage of the functional principal components which greatly facilitates the implementation and the theoretical analysis. The selection and estimation are achieved by penalized least squares using a penalty which encourages the sparse structure of the additive components. Theoretical properties such as the rate of convergence are investigated. The empirical performance is demonstrated through simulation studies and a real data application.

Structured functional additive regression in reproducing kernel Hilbert spaces.

Genomic studies of glioma sub-types have amassed new disease specific mutations, yet these only partially explain how mutations are linked to predisposition or progression. We hypothesized that microsatellite variation could expand the understanding of glioma etiology. Furthermore, germline markers for gliomas are typically undetectable; therefore we also hypothesize that the predictability of cancer-associated microsatellite loci in germline DNA may support the current hypothesis of a glioma cell of origin. In this study, "normal" germline exome sequenced DNA from the 1000 Genomes Project (n=390) were compared with exome sequences from germlines of subjects with WHO grade II and III lower-grade glioma (LGG, n=136) and WHO grade IV glioblastoma (GBM, n=252) from The Cancer Genome Atlas to identify microsatellite loci non-randomly associated with glioma. From germline data, we identified 48 GBM-specific loci, 42 Lower-grade glioma specific loci and 29 loci that distinguish GBM from LGG (p≤ 0.01). We then attempted to distinguish WHO grade II glioma (n=67) from GBM resulting in 8 informative loci. Significantly, in all glioma grades, comparisons between tumor and matched germline sequences demonstrated no significant differences in these variants (p≥ 0.01). Therefore, these microsatellite loci are considered to be components of grade-specific signatures for glioma which distinguish germline sequences of individuals with cancer from those of individuals that are "normal". In order to better understand the significance of these loci, we identified biological processes enriched in genes with these variants. Most strikingly, six helicase genes were enriched in the GBM cohort (p≤ 1.0 x10⁻³). The preservation of these glioma-specific loci could therefore serve as valuable diagnostic and therapeutic markers; especially since the heterogeneity of tumor cell populations can obscure the identification of mutations preceding a metastatic phenotype.

Somatic intronic microsatellite loci differentiate glioblastoma from lower-grade gliomas.

With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events. Among these methylation patterns, bipolar patterns are important as they may originate from allele-specific methylation (ASM) or cell-specific methylation (CSM).

Nonparametric Bayesian clustering to detect bipolar methylated genomic loci.

The pluripotent cells of the embryonic ectodermal tissues are known to be a precursor for multiple tumor types. The adaptability of these cells is a trait exploited by cancer. We previously described cancer-associated microsatellite loci (CAML) shared between glioblastoma (GBM) and lower-grade gliomas. Therefore, we hypothesized that these variants, identified from germline DNA, are shared by cancers from tissues originating from ectodermal tissues: neural tube cells (NTC) and crest cells (NCC). Using exome sequencing data from four cancers with origins to NTC and NCC, a 'signature' of loci significant to each cancer (p-value ≤ 0.01) was created and compared with previously identified CAML from breast cancer. The results of this analysis show that variant loci among the cancers with tissue origins from NTC/NCC were closely linked. Signaling pathways linked to genes with non-coding CAML genotypes revealed enriched connections to hereditary, neurological, and developmental disease or disorders. Thus, variants in genes from tissues initiating from NTC/NCC, if recurrently detected, may indicate a common etiology. Additionally, CAML genotypes from non-tumor DNA may predict cancer phenotypes and are common to shared embryonic tissues of origin. 

'Cut from the same cloth': Shared microsatellite variants among cancers link to ectodermal tissues-neural tube and crest cells.

DNA methylation is an epigenetic mechanism critical for tissue development and cell specification. Mammalian brains consist of many different types of cells with assumedly distinct DNA methylation profiles, and thus some genomic loci may demonstrate bipolar DNA methylation pattern, i.e. hypermethylated in one cell subset but hypomethylated in others. Currently, how extensive methylation patterns vary among brain cells is unknown and bipolar methylated genomic loci remain largely unexplored. In this study, we implemented a procedure to infer cell-subset specific methylated (CSM) loci from the methylomes of human and mouse frontal cortices at different developmental stages. With the genome-scale hairpin bisulfite sequencing approach, we demonstrated that the majority of CSM loci predicted likely resulted from the methylation differences among brain cells rather than from asymmetric DNA methylation between DNA double strands. Correlated with enhancer-associated histone modifications, putative CSM loci increased dramatically during early stages of brain development and were enriched for GWAS variants associated with neurological disorder-related diseases/traits. Altogether, this study provides a procedure to identify genomic regions showing methylation differences in a mixed cell population and our results suggest that a set of cis-regulatory elements are primed in early postnatal life whose functions may be compromised in human neurological disorders.

Mammalian Brain Development is Accompanied by a Dramatic Increase in Bipolar DNA Methylation.

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.

Functional CAR models for large spatially correlated functional datasets.

This paper addresses model-based Bayesian inference in the analysis of data arising from bioassay experiments. In such experiments, increasing doses of a chemical substance are given to treatment groups (usually rats or mice) for a fixed period of time (usually 2 years). The goal of such an experiment is to determine whether an increased dosage of the chemical is associated with increased probability of an adverse effect (usually presence of adenoma or carcinoma). The data consists of dosage, survival time, and the occurrence of the adverse event for each unit in the study. To determine whether such relationship exists, this paper proposes using Bayes factors to compare two probit models, the model that assumes increasing dose effects and the model that assumes no dose effect. These models account for the survival time of each unit through a Poly-k type correction. In order to increase statistical power, the proposed approach allows the incorporation of information from control groups from previous studies. The proposed method is able to handle data with very few occurrences of the adverse event. The proposed method is compared with a variation of the Peddada test via simulation and is shown to have higher power. We demonstrate the method by applying it to the two bioassay experiment datasets previously analyzed by other authors. Copyright © 2017 John Wiley & Sons, Ltd.

A Bayesian analysis of quantal bioassay experiments incorporating historical controls via Bayes factors.

Horseshoe bats have dynamic biosonar systems with interfaces for ultrasonic emission (reception) that change shape while diffracting the outgoing (incoming) sound waves. An information-theoretic analysis based on numerical and physical prototypes shows that these shape changes add sensory information (mutual information between distant shape conformations <20%), increase the number of resolvable directions of sound incidence, and improve the accuracy of direction finding. These results demonstrate that horseshoe bats have a highly effective substrate for dynamic encoding of sensory information.

Dynamic Substrate for the Physical Encoding of Sensory Information in Bat Biosonar.

Since many bat species thrive in densely vegetated habitats, echoes from foliage are likely to be of prime importance to the animals' sensory ecology, be it as clutter that masks prey echoes or as sources of information about the environment. To better understand the characteristics of foliage echoes, a new model for the process that generates these signals has been developed. This model takes leaf size and orientation into account by representing the leaves as circular disks of varying diameter. The two added leaf parameters are of potential importance to the sensory ecology of bats, e.g., with respect to landmark recognition and flight guidance along vegetation contours. The full model is specified by a total of three parameters: leaf density, average leaf size, and average leaf orientation. It assumes that all leaf parameters are independently and identically distributed. Leaf positions were drawn from a uniform probability density function, sizes and orientations each from a Gaussian probability function. The model was found to reproduce the first-order amplitude statistics of measured example echoes and showed time-variant echo properties that depended on foliage parameters. Parameter estimation experiments using lasso regression have demonstrated that a single foliage parameter can be estimated with high accuracy if the other two parameters are known a priori. If only one parameter is known a priori, the other two can still be estimated, but with a reduced accuracy. Lasso regression did not support simultaneous estimation of all three parameters. Nevertheless, these results demonstrate that foliage echoes contain accessible information on foliage type and orientation that could play a role in supporting sensory tasks such as landmark identification and contour following in echolocating bats.

A computational model for biosonar echoes from foliage.

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

Foliage echoes could play an important role in the sensory ecology of echolocating bats, but many aspects of their sensory information content remain to be explored. A realistic numerical model for these echoes could support the development of hypotheses for the relationship between foliage properties and echo parameters. In prior work by the authors, a simple foliage model based on circular disks distributed uniformly in space has been developed. In the current work, three key simplifications used in this model have been examined: (i) representing leaves as circular disks, (ii) neglecting shading effects between leaves, and (iii) the uniform spatial distribution of the leaves. The target strengths of individual leaves and shading between them have been examined in physical experiments, whereas the impact of the spatial leaf distribution has been studied by modifying the numerical model to include leaf distributions according to a biomimetic model for natural branching patterns (L-systems). Leaf samples from a single species (leatherleaf arrowwood) were found to match the relationship between size and target strength of the disk model fairly well, albeit with a large variability part of which could be due to unaccounted geometrical features of the leaves. Shading between leaf-sized disks did occur for distances below 50 cm and could hence impact the echoes. Echoes generated with L-system models in two distinct tree species (ginkgo and pine) showed consistently more temporal inhomogeneity in the envelope amplitudes than a reference with uniform distribution. However, these differences were small compared to effects found in response to changes in the relative orientation of simulated sonar beam and foliage. These findings support the utility of the uniform leaf distribution model and suggest that bats could use temporal inhomogeneities in the echoes to make inferences regarding the relative positioning of their sonar and a foliage.

A simplified model of biosonar echoes from foliage and the properties of natural foliages.

Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in  show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical  data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Identification of Differentially Methylated Sites with Weak Methylation Effects.

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design.

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models.

Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes. Motivated by ERP data in a smoking cessation study, we introduce a Bayesian spatial functional regression framework that models the entire ERPs as spatially correlated functional responses and the stimulus types as covariates. This novel framework relies on mixed models to characterize the effects of stimuli while simultaneously accounting for the multilevel correlation structure. The spatial correlation among the ERP profiles is captured through basis-space Matérn assumptions that allow either separable or nonseparable spatial correlations over time. We induce both adaptive regularization over time and spatial smoothness across electrodes via a correlated normal-exponential-gamma (CNEG) prior on the fixed effect coefficient functions. Our proposed framework includes both Gaussian models as well as robust models using heavier-tailed distributions to make the regression automatically robust to outliers. We introduce predictive methods to select among Gaussian vs. robust models and models with separable vs. non-separable spatiotemporal correlation structures. Our proposed analysis produces global tests for stimuli effects across entire time (or time-frequency) and electrode domains, plus multiplicity-adjusted pointwise inference based on experiment-wise error rate or false discovery rate to flag spatiotemporal (or spatio-temporal-frequency) regions that characterize stimuli differences, and can also produce inference for any prespecified waveform components. Our analysis of the smoking cessation ERP data set reveals numerous effects across different types of visual stimuli.

Robust and Gaussian spatial functional regression models for analysis of event-related potentials.

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.

Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells.

Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in next-generation sequencing (NGS) applications. Existing methods for calling these variants often make simplified assumptions of positional independence and fail to leverage the dependence between genotypes at nearby loci that is caused by linkage disequilibrium (LD).

Article	Year
Structured functional additive regression in reproducing kernel Hilbert spaces. Journal of the Royal Statistical Society. Series B, Statistical methodology\| PubMed ID: 25013362	2014
Somatic intronic microsatellite loci differentiate glioblastoma from lower-grade gliomas. Oncotarget\| PubMed ID: 25153720	2014
Nonparametric Bayesian clustering to detect bipolar methylated genomic loci. BMC bioinformatics\| PubMed ID: 25592753	2015
'Cut from the same cloth': Shared microsatellite variants among cancers link to ectodermal tissues-neural tube and crest cells. Oncotarget\| PubMed ID: 26246470	2015
Mammalian Brain Development is Accompanied by a Dramatic Increase in Bipolar DNA Methylation. Scientific reports\| PubMed ID: 27585862	2016
Functional CAR models for large spatially correlated functional datasets. Journal of the American Statistical Association\| PubMed ID: 28018013	2016
A Bayesian analysis of quantal bioassay experiments incorporating historical controls via Bayes factors. Statistics in medicine\| PubMed ID: 28106916	2017
Dynamic Substrate for the Physical Encoding of Sensory Information in Bat Biosonar. Physical review letters\| PubMed ID: 28452520	2017
A computational model for biosonar echoes from foliage. PloS one\| PubMed ID: 28817631	2017
Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study. Computational statistics & data analysis\| PubMed ID: 29051679	2017
A simplified model of biosonar echoes from foliage and the properties of natural foliages. PloS one\| PubMed ID: 29240840	2017
Identification of Differentially Methylated Sites with Weak Methylation Effects. Genes\| PubMed ID: 29419727	2018
A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models. Technometrics : a journal of statistics for the physical, chemical, and engineering sciences\| PubMed ID: 29749977	2018
Robust and Gaussian spatial functional regression models for analysis of event-related potentials. NeuroImage\| PubMed ID: 30057352	2018
Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells. Frontiers in genetics\| PubMed ID: 30697231	2018
vi-HMM: a novel HMM-based method for sequence variant identification in short-read data. Human genomics\| PubMed ID: 30795817	2019

Hongxiao Zhu

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