Genetics and Genomics Commons^™

Adjusting For Gene-Specific Covariates To Improve Rna-Seq Analysis, Hyeongseon Jeon, Kyu-Sang Lim, Yet Nguyen, Dan Nettleton

Mathematics & Statistics Faculty Publications

Summary

This paper suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by employing two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning …

Comparison Of Software Packages For Detecting Differentially Expressed Genes From Single-Sample Rna-Seq Data, Rong Zhou

Electronic Theses and Dissertations

RNA-sequencing (RNA-seq) has rapidly become the tool in many genome-wide transcriptomic studies. It provides a way to understand the RNA environment of cells in different physiological or pathological states to determine how cells respond to these changes. RNA-seq provides quantitative information about the abundance of different RNA species present in a given sample. If the difference or change observed in the read counts or expression level between two experimental conditions is statistically significant, the gene is declared as differentially expressed. A large number of methods for detecting differentially expressed genes (DEGs) with RNA-seq have been developed, such as the methods …

Abrf Proteome Informatics Research Group (Iprg) 2016 Study: Inferring Proteoforms From Bottom-Up Proteomics Data., Joon-Yong Lee, Hyungwon Choi, Christopher M Colangelo, Darryl Davis, Michael R Hoopmann, Lukas Käll, Henry Lam, Samuel H Payne, Yasset Perez-Riverol, Matthew The, Ryan Wilson, Susan T Weintraub, Magnus Palmblad

Articles, Abstracts, and Reports

This report presents the results from the 2016 Association of Biomolecular Resource Facilities Proteome Informatics Research Group (iPRG) study on proteoform inference and false discovery rate (FDR) estimation from bottom-up proteomics data. For this study, 3 replicate Q Exactive Orbitrap liquid chromatography-tandom mass spectrometry datasets were generated from each of 4

Multiple Testing Correction With Repeated Correlated Outcomes: Applications To Epigenetics, Katie Leap

Masters Theses

Epigenetic changes (specifically DNA methylation) have been associated with adverse health outcomes; however, unlike genetic markers that are fixed over the lifetime of an individual, methylation can change. Given that there are a large number of methylation sites, measuring them repeatedly introduces multiple testing problems beyond those that exist in a static genetic context. Using simulations of epigenetic data, we considered different methods of controlling the false discovery rate. We considered several underlying associations between an exposure and methylation over time.

We found that testing each site with a linear mixed effects model and then controlling the false discovery rate …

Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes

Jeffrey S. Morris

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the …

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 …

Hierarchical Hidden Markov Model With Application To Joint Analysis Of Chip-Chip And Chip-Seq Data, Hyungwon Choi, Debashis Ghosh, Zhaohui S. Qin

Debashis Ghosh

Motivation: Identication of transcription factor binding sites (TFBS) is a fundamental problem in understanding the mechanism of gene regulation. The ChIP-chip technology has accelerated this eort by providing a simultaneous genome-wide map of TFBS in a high-throughput fashion. Recently, a sequencing-based ChIP-seq has appeared as a promising alternative that can identify targets with an improved sensitivity/specicity in high resolution. However, studies have suggested that distinct experimental platforms can be complementary in TFBS identication. The availability of data obtained from multiple platforms motivates a meta-analysis for improved identication of candidate motifs.

Results: In this work, we propose a hierarchical hidden Markov …

Discrete Nonparametric Algorithms For Outlier Detection With Genomic Data, Debashis Ghosh

Debashis Ghosh

In high-throughput studies involving genetic data such as from gene expression microarrays, differential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of differential expression. The approach here is based on recasting multiple comparisons procedures for assessing outlying expression values. A major complication is that the resulting p-values are discrete; some theoretical properties of sequential testing procedures …

Optimal Sample Size For Multiple Testing: The Case Of Gene Expression Microarrays, Peter Muller, Giovanni Parmigiani, Christian Robert, Judith Rousseau

Johns Hopkins University, Dept. of Biostatistics Working Papers

We consider the choice of an optimal sample size for multiple comparison problems. The motivating application is the choice of the number of microarray experiments to be carried out when learning about differential gene expression. However, the approach is valid in any application that involves multiple comparisons in a large number of hypothesis tests. We discuss two decision problems in the context of this setup: the sample size selection and the decision about the multiple comparisons. We adopt a decision theoretic approach,using loss functions that combine the competing goals of discovering as many ifferentially expressed genes as possible, while keeping …