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Articles 331 - 360 of 409
Full-Text Articles in Genetics and Genomics
Genome-Wide Compensatory Changes Accompany Drug-Selected Mutations In The Plasmodium Falciparum Crt Gene, Hongying Jiang, Jigar J. Patel, Ming Yi, Jianbing Mu, Jinhui Ding, Robert Stephens, Roland A. Cooper, Michael T. Ferdig, Xin-Zhuan Su
Genome-Wide Compensatory Changes Accompany Drug-Selected Mutations In The Plasmodium Falciparum Crt Gene, Hongying Jiang, Jigar J. Patel, Ming Yi, Jianbing Mu, Jinhui Ding, Robert Stephens, Roland A. Cooper, Michael T. Ferdig, Xin-Zhuan Su
Biological Sciences Faculty Publications
Mutations in PfCRT (Plasmodium falciparum chloroquine-resistant transporter), particularly the substitution at amino acid position 76, confer chloroquine (CQ) resistance in P. falciparum. Point mutations in the homolog of the mammalian multidrug resistance gene (pfmdr1) can also modulate the levels of CQ response. Moreover, parasites with the same pfcrt and pfmdr1 alleles exhibit a wide range of drug sensitivity, suggesting that additional genes contribute to levels of CQ resistance (CQR). Reemergence of CQ sensitive parasites after cessation of CQ use indicates that changes in PfCRT are deleterious to the parasite. Some CQR parasites, however, persist in the …
Network-Constrained Regularization And Variable Selection For Analysis Of Genomic Data, Caiyan Li, Hongzhe Li
Network-Constrained Regularization And Variable Selection For Analysis Of Genomic Data, Caiyan Li, Hongzhe Li
UPenn Biostatistics Working Papers
Graphs or networks are common ways of depicting information. In biology in particular, many different biological processes are represented by graphs, such as regulatory networks or metabolic pathways. This kind of {\it a priori} information gathered over many years of biomedical research is a useful supplement to the standard numerical genomic data such as microarray gene expression data. How to incorporate information encoded by the known biological networks or graphs into analysis of numerical data raises interesting statistical challenges. In this paper, we introduce a network-constrained regularization procedure for linear regression analysis in order to incorporate the information from these …
Vertex Clustering In Random Graphs Via Reversible Jump Markov Chain Monte Carlo, Stefano Monni, Hongzhe Li
Vertex Clustering In Random Graphs Via Reversible Jump Markov Chain Monte Carlo, Stefano Monni, Hongzhe Li
UPenn Biostatistics Working Papers
Networks are a natural and effective tool to study relational data, in which observations are collected on pairs of units. The units are represented by nodes and their relations by edges. In biology, for example, proteins and their interactions, and, in social science, people and inter-personal relations may be the nodes and the edges of the network. In this paper we address the question of clustering vertices in networks, as a way to uncover homogeneity patterns in data that enjoy a network representation. We use a mixture model for random graphs and propose a reversible jump Markov chain Monte Carlo …
A Bayesian Model For Cross-Study Differential Gene Expression, Robert B. Scharpf, Hakon Tjelemeland, Giovanni Parmigiani, Andrew B. Nobel
A Bayesian Model For Cross-Study Differential Gene Expression, Robert B. Scharpf, Hakon Tjelemeland, Giovanni Parmigiani, Andrew B. Nobel
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this paper we define a hierarchical Bayesian model for microarray expression data collected from several studies and use it to identify genes that show differential expression between two conditions. Key features include shrinkage across both genes and studies; flexible modeling that allows for interactions between platforms and the estimated effect, and for both concordant and discordant differential expression across studies. We evaluated the performance of our model in a comprehensive fashion, using both artificial data, and a "split-sample" validation approach that provides an agnostic assessment of the model's behavior not only under the null hypothesis but also under a …
Assessing Population Level Genetic Instability Via Moving Average, Samuel Mcdaniel, Rebecca Betensky, Tianxi Cai
Assessing Population Level Genetic Instability Via Moving Average, Samuel Mcdaniel, Rebecca Betensky, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
Statistical Methods For The Analysis Of Cancer Genome Sequencing Data, Giovanni Parmigiani, J. Lin, Simina Boca, T. Sjoblom, K.W. Kinzler, V.E. Velculescu, B. Vogelstein
Statistical Methods For The Analysis Of Cancer Genome Sequencing Data, Giovanni Parmigiani, J. Lin, Simina Boca, T. Sjoblom, K.W. Kinzler, V.E. Velculescu, B. Vogelstein
Johns Hopkins University, Dept. of Biostatistics Working Papers
The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied.
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
Dartmouth Scholarship
Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. …
Assessment Of A Cgh-Based Genetic Instability, David A. Engler, Yiping Shen, J F. Gusella, Rebecca A. Betensky
Assessment Of A Cgh-Based Genetic Instability, David A. Engler, Yiping Shen, J F. Gusella, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Harvard University Biostatistics Working Paper Series
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time …
The Integrative Correlation Coefficient: A Measure Of Cross-Study Reproducibility For Gene Expressionea Array Data, Leslie M. Cope, Liz Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani
The Integrative Correlation Coefficient: A Measure Of Cross-Study Reproducibility For Gene Expressionea Array Data, Leslie M. Cope, Liz Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
Multi-study analysis adds value to microarray experiments. However, because of significant technical differences between microarray platforms, and because of differences in study design, it can be difficult to combine data. We have developed a statistical measure of reproducibility that can be applied to individual genes, measured in two different studies. This statistic, which we call the Integrative Correlation Coefficient or Correlation of Correlations, borrows strength across many genes to estimate the strength of the relationship between expression values in the two studies.
What Is The Best Reference Rna? And Other Questions Regarding The Design And Analysis Of Two-Color Microarray Experiments, Kathleen F. Kerr, Kyle A. Serikawa, Caimiao Wei, Mette A. Peters, Roger E. Bumgarner
What Is The Best Reference Rna? And Other Questions Regarding The Design And Analysis Of Two-Color Microarray Experiments, Kathleen F. Kerr, Kyle A. Serikawa, Caimiao Wei, Mette A. Peters, Roger E. Bumgarner
UW Biostatistics Working Paper Series
The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness …
A Markov Random Field Model For Network-Based Analysis Of Genomic Data, Zhi Wei, Hongzhe Li
A Markov Random Field Model For Network-Based Analysis Of Genomic Data, Zhi Wei, Hongzhe Li
UPenn Biostatistics Working Papers
A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed genes do not utilize the known pathway information in the phase of identifying such genes. In this paper, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the …
Statistical Methods For Inference Of Genetic Networks And Regulatory Modules, Hongzhe Li
Statistical Methods For Inference Of Genetic Networks And Regulatory Modules, Hongzhe Li
UPenn Biostatistics Working Papers
Large-scale microarray gene expression data, motif data derived from promotor sequences, genome-wide chromatin immunoprecipitation (ChIP-chip) data, DNA polymorphism data and epigenomic data provide the possibility of constructing genetic networks or biological pathways, especially regulatory networks. In this paper, we review some new statistical methods for inference of genetic networks and regulatory modules, including a threshold gradient descent procedure for inference of Gaussian graphical models, a sparse regression mixture modeling approach for inference of regulatory modules, and the varying coefficient model for identifying regulatory subnetworks by integrating microarray time-course gene expression data and motif or ChIP-chip data. We present the statistical …
Conservative Estimation Of Optimal Multiple Testing Procedures, James E. Signorovitch
Conservative Estimation Of Optimal Multiple Testing Procedures, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Hidden Markov Model For Joint Estimation Of Genotype And Copy Number In High-Throughput Snp Chips, Robert B. Scharpf, Giovanni Parmigiani, Jonathan Pevnser, Ingo Ruczinski
A Hidden Markov Model For Joint Estimation Of Genotype And Copy Number In High-Throughput Snp Chips, Robert B. Scharpf, Giovanni Parmigiani, Jonathan Pevnser, Ingo Ruczinski
Johns Hopkins University, Dept. of Biostatistics Working Papers
Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome-wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput …
Power Boosting In Genome-Wide Studies Via Methods For Multivariate Outcomes, Mary J. Emond
Power Boosting In Genome-Wide Studies Via Methods For Multivariate Outcomes, Mary J. Emond
UW Biostatistics Working Paper Series
Whole-genome studies are becoming a mainstay of biomedical research. Examples include expression array experiments, comparative genomic hybridization analyses and large case-control studies for detecting polymorphism/disease associations. The tactic of applying a regression model to every locus to obtain test statistics is useful in such studies. However, this approach ignores potential correlation structure in the data that could be used to gain power, particularly when a Bonferroni correction is applied to adjust for multiple testing. In this article, we propose using regression techniques for misspecified multivariate outcomes to increase statistical power over independence-based modeling at each locus. Even when the outcome …
Data Quality Assessment Of Ungated Flow Cytometry Data In High, Nolwenn Le Meur, Anthony Rossini, Maura Gasparetto, Clay Smith, Ryan R. Brinkman, Robert Gentleman
Data Quality Assessment Of Ungated Flow Cytometry Data In High, Nolwenn Le Meur, Anthony Rossini, Maura Gasparetto, Clay Smith, Ryan R. Brinkman, Robert Gentleman
Bioconductor Project Working Papers
Background: The recent development of semi-automated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data.
Methods: We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized …
Group Scad Regression Analysis For Microarray Time Course Gene Expression Data, Lifeng Wang, Guang Chen, Hongzhe Li Phd
Group Scad Regression Analysis For Microarray Time Course Gene Expression Data, Lifeng Wang, Guang Chen, Hongzhe Li Phd
UPenn Biostatistics Working Papers
Since many important biological systems or processes are dynamic systems, it is important to study the gene expression patterns over time in a genomic scale in order to capture the dynamic behavior of gene expression. Microarray technologies have made it possible to measure the gene expression levels of essentially all the genes during a given biological process. In order to determine the transcriptional factors involved in gene regulation during a given biological process, we propose to develop a functional response model with varying coefficients in order to model the transcriptional effects on gene expression levels and to develop a group …
Trab: Testing Whether Mutation Frequencies Are Above An Unknown Background, Giovanni Parmigiani, Sining Chen, Victor E. Velculescu
Trab: Testing Whether Mutation Frequencies Are Above An Unknown Background, Giovanni Parmigiani, Sining Chen, Victor E. Velculescu
Johns Hopkins University, Dept. of Biostatistics Working Papers
To rigorously determine whether a gene or a population of genes have alterations that are involved in carcinogenesis requires comparison of the prevalence of identified changes to the background mutation frequency present in tumor DNA. To facilitate this task, we develop a testing approach and the associated R library, called TRAB, that evaluates whether the frequency of somatic mutation is higher than an unknown, but estimable, background. We test the null hypothesis that the frequency belongs to background population of frequencies against the alternative hypothesis that the frequency is higher. Background mutation frequencies are themselves allowed to be variable. TRAB …
Optimized Cross-Study Analysis Of Microarray-Based Predictors, Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani
Optimized Cross-Study Analysis Of Microarray-Based Predictors, Xiaogang Zhong, Luigi Marchionni, Leslie Cope, Edwin S. Iversen, Elizabeth S. Garrett-Mayer, Edward Gabrielson, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
Background: Microarray-based gene expression analysis is widely used in cancer research to discover molecular signatures for cancer classification and prediction. In addition to numerous independent profiling projects, a number of investigators have analyzed multiple published data sets for purposes of cross-study validation. However, the diverse microarray platforms and technical approaches make direct comparisons across studies difficult, and without means to identify aberrant data patterns, less than optimal. To address this issue, we previously developed an integrative correlation approach to systematically address agreement of gene expression measurements across studies, providing a basis for cross-study validation analysis. Here we generalize this methodology …
Improving Gsea For Analysis Of Biologic Pathways For Differential Gene Expression Across A Binary Phenotype , Irina Dinu, John D. Potter, Thomas Mueller, Qi Liu, Adeniyi J. Adewale, Gian S. Jhangri, Gunilla Einecke, Konrad S. Famulski, Philip Halloran, Yutaka Yasui
Improving Gsea For Analysis Of Biologic Pathways For Differential Gene Expression Across A Binary Phenotype , Irina Dinu, John D. Potter, Thomas Mueller, Qi Liu, Adeniyi J. Adewale, Gian S. Jhangri, Gunilla Einecke, Konrad S. Famulski, Philip Halloran, Yutaka Yasui
COBRA Preprint Series
Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of single genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the single-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Specifically, we illustrate, in a simulation study, that GSEA gives statistical significance to gene sets that have no gene associated …
The Plant Structure Ontology, A Unified Vocabulary Of Anatomy And Morphology Of A Flowering Plant, Katica Ilic, Elizabeth Kellogg, Pankaj Jaiswal, Felipe Zapata, Peter Stevens, Leszek Vincent, Shulamit Avraham, Leonore Reiser, Anuradha Pujar, Martin Sachs, Noah Whitman, Susan Mccouch, Mary Schaeffer, Doreen Ware, Lincoln Stein, Seung Rhee
The Plant Structure Ontology, A Unified Vocabulary Of Anatomy And Morphology Of A Flowering Plant, Katica Ilic, Elizabeth Kellogg, Pankaj Jaiswal, Felipe Zapata, Peter Stevens, Leszek Vincent, Shulamit Avraham, Leonore Reiser, Anuradha Pujar, Martin Sachs, Noah Whitman, Susan Mccouch, Mary Schaeffer, Doreen Ware, Lincoln Stein, Seung Rhee
Biology Department Faculty Works
Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological …
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Penalized Likelihood And Bayesian Methods For Sparse Contingency Tables: An Analysis Of Alternative Splicing In Full-Length Cdna Libraries, Corinne Dahinden, Giovanni Parmigiani, Mark C. Emerick, Peter Buhlmann
Penalized Likelihood And Bayesian Methods For Sparse Contingency Tables: An Analysis Of Alternative Splicing In Full-Length Cdna Libraries, Corinne Dahinden, Giovanni Parmigiani, Mark C. Emerick, Peter Buhlmann
Johns Hopkins University, Dept. of Biostatistics Working Papers
We develop methods to perform model selection and parameter estimation in loglinear models for the analysis of sparse contingency tables to study the interaction of two or more factors. Typically, datasets arising from so-called full-length cDNA libraries, in the context of alternatively spliced genes, lead to such sparse contingency tables. Maximum Likelihood estimation of log-linear model coefficients fails to work because of zero cell entries. Therefore new methods are required to estimate the coefficients and to perform model selection. Our suggestions include computationally efficient penalization (Lasso-type) approaches as well as Bayesian methods using MCMC. We compare these procedures in a …
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for …
Estimating Genome-Wide Copy Number Using Allele Specific Mixture Models, Wenyi Wang , Benilton Caravalho, Nate Miller, Jonathan Pevsner, Aravinda Chakravarti, Rafael A. Irizarry
Estimating Genome-Wide Copy Number Using Allele Specific Mixture Models, Wenyi Wang , Benilton Caravalho, Nate Miller, Jonathan Pevsner, Aravinda Chakravarti, Rafael A. Irizarry
Johns Hopkins University, Dept. of Biostatistics Working Papers
Genomic changes such as copy number alterations are thought to be one of the major underlying causes of human phenotypic variation among normal and disease subjects [23,11,25,26,5,4,7,18]. These include chromosomal regions with so-called copy number alterations: instead of the expected two copies, a section of the chromosome for a particular individual may have zero copies (homozygous deletion), one copy (hemizygous deletions), or more than two copies (amplifications). The canonical example is Down syndrome which is caused by an extra copy of chromosome 21. Identification of such abnormalities in smaller regions has been of great interest, because it is believed to …
Exploration Of Distributional Models For A Novel Intensity-Dependent Normalization , Nicola Lama, Patrizia Boracchi, Elia Mario Biganzoli
Exploration Of Distributional Models For A Novel Intensity-Dependent Normalization , Nicola Lama, Patrizia Boracchi, Elia Mario Biganzoli
COBRA Preprint Series
Currently used gene intensity-dependent normalization methods, based on regression smoothing techniques, usually approach the two problems of location bias detrending and data re-scaling without taking into account the censoring characteristic of certain gene expressions produced by experiment measurement constraints or by previous normalization steps. Moreover, the bias vs variance balance control of normalization procedures is not often discussed but left to the user's experience. Here an approximate maximum likelihood procedure to fit a model smoothing the dependences of log-fold gene expression differences on average gene intensities is presented. Central tendency and scaling factor were modeled by means of B-splines smoothing …
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.