Genetics and Genomics Commons^™

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

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

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

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

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

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

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

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

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

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 …

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

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

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

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

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

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

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

Harvard University Biostatistics Working Paper Series

No abstract provided.

Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Group Additive Regression Models For Genomic Data Analysis, Yihui Luan, Hongzhe Li

UPenn Biostatistics Working Papers

One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group …

Extensions To Gene Set Enrichment, Zhen Jiang, Robert Gentleman

Bioconductor Project Working Papers

Motivation: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture moderate but coordinated changes in the expression of sets of functionally related genes. We propose number of extensions to GSEA, which uses different statistics to describe the association between genes and phenotype of interest. We make use of dimension reduction procedures, such as principle component analysis to identify gene sets containing coordinated genes. We also address the problem of overlapping among gene sets in this paper.

Results: We applied our methods to the data come from a clinical trial in acute lymphoblastic leukemia (ALL) [1]. We identified interesting …

Circadian Rhythmicity By Autocatalysis, Arun Mehra, Christian I. Hong, Mi Shi, Jennifer J. Loros, Jay C. Dunlap, Peter Ruoff

Dartmouth Scholarship

The temperature compensated in vitro oscillation of cyanobacterial KaiC phosphorylation, the first example of a thermodynamically closed system showing circadian rhythmicity, only involves the three Kai proteins (KaiA, KaiB, and KaiC) and ATP. In this paper, we describe a model in which the KaiA- and KaiB-assisted autocatalytic phosphorylation and dephosphorylation of KaiC are the source for circadian rhythmicity. This model, based upon autocatalysis instead of transcription-translation negative feedback, shows temperature-compensated circadian limit-cycle oscillations with KaiC phosphorylation profiles and has period lengths and rate constant values that are consistent with experimental observations.

Fdr And Bayesian Multiple Comparisons Rules, Peter Muller, Giovanni Parmigiani, Kenneth Rice

Johns Hopkins University, Dept. of Biostatistics Working Papers

We discuss Bayesian approaches to multiple comparison problems, using a decision theoretic perspective to critically compare competing approaches. We set up decision problems that lead to the use of FDR-based rules and generalizations. Alternative definitions of the probability model and the utility function lead to different rules and problem-specific adjustments. Using a loss function that controls realized FDR we derive an optimal Bayes rule that is a variation of the Benjamini and Hochberg (1995) procedure. The cutoff is based on increments in ordered posterior probabilities instead of ordered p- values. Throughout the discussion we take a Bayesian perspective. In particular, …

Exploration, Normalization, And Genotype Calls Of High Density Oligonucleotide Snp Array Data, Benilton Carvalho, Terence P. Speed, Rafael A. Irizarry

Johns Hopkins University, Dept. of Biostatistics Working Papers

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications …

Multivariate Analysis And Visualization Of Splicing Correlations In Single-Gene Transcriptomes, Mark C. Emerick, Giovanni Parmigiani, William S. Agnew

Johns Hopkins University, Dept. of Biostatistics Working Papers

Through ‘combinatorial splicing’, RNA metabolism may create enormous structural diversity in the proteome. Functional interactions among multiple alternative domains can have a disproportionate impact on the phenotype, requiring integrated RNA-level regulation of molecular composition. Splicing correlations within molecules expressed from a single gene, where these effects would be greatest, provide valuable clues to functional relationships and targets for splicing regulation. We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in ‘clock plots’ and linkage grids. Higher-order correlations are assessed via a loglinear model and Monte Carlo analysis with an …

Plasq: A Generalized Linear Model-Based Procedure To Determine Allelic Dosage Ini Cancer Cells From Snp Array Data, Thomas Laframboise, David P. Harrington, Barbara A. Weir

Harvard University Biostatistics Working Paper Series

No abstract provided.

Desulfovibrio Desulfuricans G20 Tetraheme Cytochrome Structure At 1.5 A˚ And Cytochrome Interaction With Metal Complexes, Mrunalini Pattarkine, J J. Tanner, C A. Bottoms, Y H. Lee, Judy D. Wall

Faculty Works

The structure of the type I tetraheme cytochrome c3 from Desulfovibrio desulfuricans G20 was determined to 1.5 A˚ by X-ray crystallography. In addition to the oxidized form, the structure of the molybdate-bound form of the protein was determined from oxidized crystals soaked in sodium molybdate. Only small structural shifts were obtained with metal binding, consistent with the remarkable structural stability of this protein. In vitro experiments with pure cytochrome showed that molybdate could oxidize the reduced cytochrome, although not as rapidly as U(VI) present as uranyl acetate. Alterations in the overall conformation and thermostability of the metal-oxidized protein were investigated …

Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross

Dartmouth Scholarship

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.

Defocused Orientation And Position Imaging (Dopi) Of Myosin V, Rolfe G. Petschek

Faculty Scholarship

The centroid of a fluorophore can be determined within 1.5-nm accuracy from its focused image through fluorescence imaging with one-nanometer accuracy (FIONA). If, instead, the sample is moved away from the focus, the point-spread-function depends on both the position and 3D orientation of the fluorophore, which can be calculated by defocused orientation and position imaging (DOPI). DOPI does not always yield position accurately, but it is possible to switch back and forth between focused and defocused imaging, thereby getting the centroid and the orientation with precision. We have measured the 3D orientation and stepping behavior of single bifunctional rhodamine probes …