Computational Biology Commons^™

Frozen Robust Multi-Array Analysis (Frma), Matthew N. Mccall, Benjamin M. Bolstad, Rafael A. Irizarry

Johns Hopkins University, Dept. of Biostatistics Working Papers

Robust Multi-array Analysis (RMA) is the most widely used preprocessing algorithm for Affymetrix and Nimblegen gene-expression microarrays. RMA performs background correction, normalization, and summarization in a modular way. The last two steps require multiple arrays to be analyzed simultaneously. The ability to borrow information across samples provides RMA various advantages. For example, the summarization step fits a parametric model that accounts for probe-effects, assumed to be fixed across arrays, and improves outlier detection. Residuals, obtained from the fitted model, permit the creation of useful quality metrics. However, the dependence on multiple arrays has two drawbacks: (1) RMA can- not be …

Resampling-Based Multiple Hypothesis Testing With Applications To Genomics: New Developments In The R/Bioconductor Package Multtest, Houston N. Gilbert, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

The multtest package is a standard Bioconductor package containing a suite of functions useful for executing, summarizing, and displaying the results from a wide variety of multiple testing procedures (MTPs). In addition to many popular MTPs, the central methodological focus of the multtest package is the implementation of powerful joint multiple testing procedures. Joint MTPs are able to account for the dependencies between test statistics by effectively making use of (estimates of) the test statistics joint null distribution. To this end, two additional bootstrap-based estimates of the test statistics joint null distribution have been developed for use in the …

Evaluation Of Statistical Methods For Normalization And Differential Expression In Mrna-Seq Experiments, James H. Bullard, Elizabeth A. Purdom, Kasper D. Hansen, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

The focus of this article is on the design and analysis of mRNA-Seq experiments, with the aim of inferring transcript levels and identifying differentially expressed genes. We investigate two mRNA-Seq datasets obtained using Illumina's Genome Analyzer platform to measure transcript levels in reference samples considered in the MicroArray Quality Control (MAQC) Project. We address the following four main issues: (1) exploratory data analysis for mapped reads, relating read counts to variables describing input samples and genomic regions of interest; (2) assessment and quantitation of biological effects (e.g., expression levels in Brain vs. UHR) and nuisance experimental effects (e.g., library preparation, …

Gene Set Enrichment Analysis Made Simple, Rafael A. Irizarry, Chi Wang, Yun Zhou, Terence P. Speed

Johns Hopkins University, Dept. of Biostatistics Working Papers

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, chose an appropriate cut-off, and create a list of candidate genes. This approach has been criticized for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, many of these methods seem overly complicated. Furthermore, the most popular method, Gene Set Enrichment Analysis …

Generalized Liquid Association, Yen-Yi Ho, Leslie Cope, Thomas A. Louis, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

The analysis of interactions among a group of genes is fundamental to fur- ther our understanding of their biological interactions in a cell. Several studies suggested that the co-expression relationship of two genes can be modulated by a third controller gene. These controller genes and the corresponding modulated co-expressed gene pairs are the subjects of interests in this study. This described \controller-modulated genes" three-way interactions is referred as liquid association in the literature. Analysis of gene expression data has suggested that these interactions are present in many biological systems.

To quantify the magnitude of liquid association for a given gene …

Joint Multiple Testing Procedures For Graphical Model Selection With Applications To Biological Networks, Houston N. Gilbert, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

Gaussian graphical models have become popular tools for identifying relationships between genes when analyzing microarray expression data. In the classical undirected Gaussian graphical model setting, conditional independence relationships can be inferred from partial correlations obtained from the concentration matrix (= inverse covariance matrix) when the sample size n exceeds the number of parameters p which need to estimated. In situations where n < p, another approach to graphical model estimation may rely on calculating unconditional (zero-order) and first-order partial correlations. In these settings, the goal is to identify a lower-order conditional independence graph, sometimes referred to as a ‘0-1 graphs’. For either choice of graph, model selection may involve a multiple testing problem, in which edges in a graph are drawn only after rejecting hypotheses involving (saturated or lower-order) partial correlation parameters. Most multiple testing procedures applied in previously proposed graphical model selection algorithms rely on standard, marginal testing methods which do not take into account the joint distribution of the test statistics derived from (partial) correlations. We propose and implement a multiple testing framework useful when testing for edge inclusion during graphical model selection. Two features of our methodology include (i) a computationally efficient and asymptotically valid test statistics joint null distribution derived from influence curves for correlation-based parameters, and (ii) the application of empirical Bayes joint multiple testing procedures which can effectively control a variety of popular Type I error rates by incorpo- rating joint null distributions such as those described here (Dudoit and van der Laan, 2008). Using a dataset from Arabidopsis thaliana, we observe that the use of more sophisticated, modular approaches to multiple testing allows one to identify greater numbers of edges when approximating an undirected graphical model using a 0-1 graph. Our framework may also be extended to edge testing algorithms for other types of graphical models (e.g., for classical undirected, bidirected, and directed acyclic graphs).

A Novel Topology For Representing Protein Folds, Mark R. Segal

COBRA Preprint Series

Various topologies for representing three dimensional protein structures have been advanced for purposes ranging from prediction of folding rates to ab initio structure prediction. Examples include relative contact order, Delaunay tessellations, and backbone torsion angle distributions. Here we introduce a new topology based on a novel means for operationalizing three dimensional proximities with respect to the underlying chain. The measure involves first interpreting a rank-based representation of the nearest neighbors of each residue as a permutation, then determining how perturbed this permutation is relative to an unfolded chain. We show that the resultant topology provides improved association with folding and …

Sparse Linear Discriminant Analysis For Simultaneous Testing For The Significance Of A Gene Set/Pathway And Gene Selection, Michael C. Wu, Lingson Zhang, Zhaoxi Wang, David C. Christiani, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Detection Of Recurrent Copy Number Alterations In The Genome: A Probabilistic Approach, Oscar M. Rueda, Ramon Diaz-Uriarte

COBRA Preprint Series

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases (including cancer, HIV acquisition, autoimmune and neurodegenerative diseases), and array-based CGH (aCGH) is currently the main technology to locate CNVs. Several methods can analyze aCGH data at the single sample level, but disease-critical genes are more likely to be found in regions that are common or recurrent among samples. Unfortunately, defining recurrent CNV regions remains a challenge. Moreover, the heterogeneous nature of many diseases requires that we search for CNVs that affect only some subsets of the samples (without prior knowledge of which regions and …

Finding Recurrent Regions Of Copy Number Variation: A Review, Oscar M. Rueda, Ramon Diaz-Uriarte

COBRA Preprint Series

Copy number variation (CNV) in genomic DNA is linked to a variety of human diseases, and array-based CGH (aCGH) is currently the main technology to locate CNVs. Although many methods have been developed to analyze aCGH from a single array/subject, disease-critical genes are more likely to be found in regions that are common or recurrent among subjects. Unfortunately, finding recurrent CNV regions remains a challenge. We review existing methods for the identification of recurrent CNV regions. The working definition of ``common'' or ``recurrent'' region differs between methods, leading to approaches that use different types of input (discretized output from a …

The Strength Of Statistical Evidence For Composite Hypotheses With An Application To Multiple Comparisons, David R. Bickel

COBRA Preprint Series

The strength of the statistical evidence in a sample of data that favors one composite hypothesis over another may be quantified by the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function. Unlike the p-value and the Bayes factor, this measure of evidence is coherent in the sense that it cannot support a hypothesis over any hypothesis that it entails. Further, when comparing the hypothesis that the parameter lies outside a non-trivial interval to the hypotheses that it lies within the interval, the proposed measure of evidence almost always asymptotically favors the correct hypothesis …

A Network-Constrained Empirical Bayes Method For Analysis Of Genomic Data, Caiyan Li, Zhi Wei, Hongzhe Li

UPenn Biostatistics Working Papers

Empirical Bayes methods are widely used in the analysis of microarray gene expression data in order to identify the differentially expressed genes or genes that are associated with other general phenotypes. Available methods often assume that genes are independent. However, genes are expected to function interactively and to form molecular modules to affect the phenotypes. In order to account for regulatory dependency among genes, we propose in this paper a network-constrained empirical Bayes method for analyzing genomic data in the framework of general linear models, where the dependency of genes is modeled by a discrete Markov random field model defined …

Estimation And Testing For The Effect Of A Genetic Pathway On A Disease Outcome Using Logistic Kernel Machine Regression Via Logistic Mixed Models, Dawei Liu, Debashis Ghosh, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Powerful And Flexible Multilocus Association Test For Quantitative Traits, Lydia Coulter Kwee, Dawei Liu, Xihong Lin, Debashis Ghosh, Michael P. Epstein

Harvard University Biostatistics Working Paper Series

No abstract provided.

Model-Based Clustering Of Methylation Array Data: A Recursive-Partitioning Algorithm For High-Dimensional Data Arising As A Mixture Of Beta Distributions, E. Andres Houseman, Brock C. Christensen, Ru-Fang Yeh, Carmen J. Marsit, Margaret R. Karagas, Margaret Wrensch, Heather H. Nelson, Joseph Wiemels, Shichun Zheng, John K. Wiencke, Karl T. Kelsey

Harvard University Biostatistics Working Paper Series

No abstract provided.

Incorporation Of Genetic Pathway Information Into Analysis Of Multivariate Gene Expression Data, Zhi Wei, Jane E. Minturn, Eric Rappaport, Garrett Brodeur, Hongzhe Li

UPenn Biostatistics Working Papers

Abstract: Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time and pathways that are perturbed during a given biological process. In this paper, we develop a hidden Markov random field model for multivariate expression data in order to identify genes and subnetworks that are related to biological processes, where the dependency of the differential expression patterns of …

Likelihood Estimation Of Conjugacy Relationships In Linear Models With Applications To High-Throughput Genomics, Brian S. Caffo, Liu Dongmei, Robert Scharpf, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately …

Empirical Null And False Discovery Rate Inference For Exponential Families, Armin Schwartzman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Assessing The Role Of Multi-Protein Complexes In Determining Phenotype, Nolwenn Le Meur, Robert Gentleman

Bioconductor Project Working Papers

Understanding regulatory mechanisms in complex biological systems is an important challenge, in particular to understand disease mechanisms, and to discover new therapies and drugs. In this paper, we consider the important question of cellular regulation of phenotype. Using single gene deletion data, we address the problem of linking a phenotype to underlying functional roles in the organism and provide a sound computational and statistical paradigm that can be extended to address more complex experimental settings such as multiple deletions. We apply the proposed approaches to publicly available data sets to demonstrate strong evidence for the involvement of multi-protein complexes in …

Design And Analysis Issues In Genome-Wide Somatic Mutation Studies Of Cancer, Giovanni Parmigiani, Simina Boca, Jimmy Lin, Kenneth W. Kinzler, Victor E. Velculescu, Bert Vogelstein

Johns Hopkins University, Dept. of Biostatistics Working Papers

The availability of the human genome sequence and progress in sequencing and bioinformatic technologies have enabled genome-wide investigation of somatic mu- tations in human cancers. This article briefly reviews challenges arising in the statistical analysis of mutational data of this kind. A first challenge is that of designing studies that efficiently allocate sequencing resources. We show that this can be addressed by two-stage designs, and demonstrate via simulations that even relatively small studies can produce lists of candidate cancer genes that are highly informative for future research efforts. A second challenge is to distinguish mutated genes that are selected for …

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

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

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

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

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.

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

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

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

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

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 …