613 research outputs found
Statistical Methods in Topological Data Analysis for Complex, High-Dimensional Data
The utilization of statistical methods an their applications within the new
field of study known as Topological Data Analysis has has tremendous potential
for broadening our exploration and understanding of complex, high-dimensional
data spaces. This paper provides an introductory overview of the mathematical
underpinnings of Topological Data Analysis, the workflow to convert samples of
data to topological summary statistics, and some of the statistical methods
developed for performing inference on these topological summary statistics. The
intention of this non-technical overview is to motivate statisticians who are
interested in learning more about the subject.Comment: 15 pages, 7 Figures, 27th Annual Conference on Applied Statistics in
Agricultur
STATISTICAL THRESHOLD VALUES FOR LOCATING QUANTITATIVE TRAIT LOCI
The detection and location of quantitative trait loci (QTL) that control quantitative characters is a problem of great interest to the genetic mapping community. Interval mapping has proved to be a useful tool in locating QTL, but has recently been challenged by faster, more sophisticated regression methods (e.g. .. composite interval mapping). Regardless of the method used to locate QTL. the distribution of the test statistic (LOD score or likelihood ratio test) is unknown. Due to the quantitative trait values following a mixture distribution rather than a single distribution, the asymptotic distribution of the test statistic is not from a standard family, such as chi-square. The purpose of this work is to introduce interval mapping, discuss the distribution of the resulting test statistic, and then present empirical threshold values for the declaration of major QTL. as well as minor QTL. Empirical threshold values are obtained by permuting the actual experimental trait data, under a fixed and known genetic map. for the purpose of representing the distribution of the test statistic under the null hypothesis of no QTL effect. Not only is a permutation test statistically justified in this case, the test reflects the specifics of the experimental situation under investigation (i. e., sample size, marker density, skewing, etc.), and may be used in a conditional sense to derive thresholds for minor QTL once a major effect has been determined
Combining Affymetrix microarray results
BACKGROUND: As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis. RESULTS: We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis. CONCLUSION: The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest
A NON-PARAMETRIC EMPIRICAL BAYES APPROACH FOR ESTIMATING TRANSCRIPT ABUNDANCE IN UN-REPLICATED NEXT-GENERATION SEQUENCING DATA
Empirical Bayes approaches have been widely used to analyze data from high throughput sequencing devices. These approaches rely on borrowing information available for all the genes across samples to get better estimates of gene level expression. To date, transcript abundance in data from next generation sequencing (NGS) technologies has been estimated using parametric approaches for analyzing count data, namely – gamma-Poisson model, negative binomial model, and over-dispersed logistic model. One serious limitation of these approaches is they cannot be applied in absence of replication. The high cost of NGS technologies imposes a serious restriction on the number of biological replicates that can be assessed. In this work, a simple non–parametric empirical Bayes modeling approach is suggested for the estimation of transcript abundances in un-replicated NGS data. The empirical Bayes analysis of NGS data follows naturally from the empirical Bayes analysis of microarray data by modifying the distributional assumption on the observations. The analysis is presented for transcript abundance estimation for two treatment groups in an un-replicated experiment, but it is easily extended for more treatment groups and replicated experiments
CORRECTING FOR AMPLIFICATION BIAS IN NEXT-GENERATION SEQUENCING DATA
Next-generation sequencing (NGS) technologies have opened the door to a wealth of knowledge and information about biological systems, particularly in genomics and epigenomics. These tools, although useful, carry with them additional technological and statistical challenges that need to be understood and addressed. One such issue is ampli cation bias. Specifically, the majority of NGS technologies effectively sample small amounts of DNA or RNA that are amplified (i.e., copied) prior to sequencing. The amplification process is not perfect, and thus sequenced read counts can be extremely biased. Unfortunately, current amplification bias controlling procedures introduce a dependence of gene expression on gene length, which effectively masks the effects of short genes with high transcription rates. In this work we present a novel procedure to account for amplification bias and demonstrate its effectiveness in estimating true gene expression independent of gene length
THE NUANCES OF STATISTICALLY ANALYZING NEXT-GENERATION SEQUENCING DATA
High-throughput sequencing technologies, in particular next-generation sequencing (NGS) technologies, have emerged as the preferred approach for exploring both gene function and pathway organization. Data from NGS technologies pose new computational and statistical challenges because of their massive size, limited replicate information, large number of genes (high-dimensionality), and discrete form. They are more complex than data from previous high-throughput technologies such as microarrays. In this work we focus on the statistical issues in analyzing and modeling NGS data for selecting genes suitable for further exploration and present a brief review of the relevant statistical methods. We discuss visualization methods to assess the suitability of statistical models for these data, statistical methods for modeling differential gene expression, and methods for checking goodness of fit of the models for NGS data. We also outline areas for further research, especially in the computational, statistical, and visualization aspects of such data
A BAYESIAN AND COVARIATE APPROACH TO COMBINE RESULTS FROM MULTIPLE MICROARRAY STUDIES
The growing popularity of microarray technology for testing changes in gene expression has resulted in multiple laboratories independently seeking to identify genes related to the same disease in the same organism. Despite the uniform nature of the technology, chance variation and fundamental differences between laboratories can result in considerable disagreement between the lists of significant candidate genes from each laboratory. By adjusting for known differences between laboratories through the use of covariates and employing a Bayesian framework to effectively account for between-laboratory variability, the results of multiple similar studies can be systematically combined via a meta-analysis. Meta-analyses yield additional information not available from any single study and provide a clearer understanding of each gene’s true relationship to the disease of interest. A simulation model based on the Barley Affymetrix GeneChip microarray demonstrates the utility of this approach. Further illustration is provided from a mouse model for multiple sclerosis
ISSUES IN TESTING DNA METHYLATION USING NEXT-GENERATION SEQUENCING
DNA methylation is an epigenetic modification known to affect gene expression, cellular differentiation, as well as phenotypes. Recent advancements in next-generation sequencing technologies have provided unparalleled insight into the location and function of DNA methylation in a variety of organisms. These data require vastly different statistical procedures than data from previous genomic-based technologies. We outline the biological and chemical processes involved in several approaches for gaining DNA methylation data. The implications of the differences between the approaches are discussed relative to the statistical methodology, and the use of genome annotation is explored for the purpose of improving the statistical power when testing for differential methylation
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