Microarrays Commons^™

Latent Growth Model Approach To Characterize Maternal Prenatal Dna Methylation Trajectories, Dana Lapato

Theses and Dissertations

Background. DNA methylation (DNAm) is a removable chemical modification to the DNA sequence intimately associated with genomic stability, cellular identity, and gene expression. DNAm patterning reflects joint contributions from genetic, environmental, and behavioral factors. As such, differences in DNAm patterns may explain interindividual variability in risk liability for complex traits like major depression (MD). Hundreds of significant DNAm loci have been identified using cross-sectional association studies. This dissertation builds on that foundational work to explore novel statistical approaches for longitudinal DNAm analyses. Methods. Repeated measures of genome-wide DNAm and social and environmental determinants of health were collected up to six …

Super Learner In Prediction, Eric Polley, Mark Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Super learning is a general loss based learning method that has been proposed and analyzed theoretically in van der Laan et al. (2007). In this article we consider super learning for prediction. The super learner is a prediction method designed to find the optimal combination of a collection of prediction algorithms. The super learner algorithm finds the combination of algorithms minimizing the cross-validated risk. The super learner framework is built on the theory of cross-validation and allows for a general class of prediction algorithms to be considered for the ensemble. Due to the previously established oracle results for the cross-validation …

Statistical Inference For Simultaneous Clustering Of Gene Expression Data, Katherine Pollard, Mark Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as …

Classification Using Generalized Partial Least Squares, Robert Gentleman, Beiying Ding

Bioconductor Project Working Papers

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in …

Statistical Approaches Of Gene Set Analysis With Quantitative Trait Loci For High-Throughput Genomic Studies., Samarendra Das

Electronic Theses and Dissertations

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on …

A Powerful And Flexible Multilocus Association Test For Quantitative Traits, Dawei Liu, Xihong Lin, Michael Epstein

Harvard University Biostatistics Working Paper Series

No abstract provided.

Cluster Analysis Of Genomic Data With Applications In R, Katherine Pollard

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in R. We discuss statistical issues and methods in choosing the number of clusters, the choice of clustering algorithm, and the choice of dissimilarity matrix. In particular, we illustrate how the bootstrap can be employed as a statistical method in cluster analysis to establish the reproducibility of the clusters and the overall variability of the followed procedure. We also show how to visualize a clustering result by plotting ordered dissimilarity matrices in R. We present a new R package, hopach, which implements the hybrid clustering method, …

Microarray Data From A Statistician’S Point Of View, Johanna Hardin

Pomona Faculty Publications and Research

No abstract provided.

On Comparing The Clustering Of Regression Models Method With K-Means Clustering, Steven Self, Li-Xuan Qin

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Gene clustering is a common question addressed with microarray data. Previous methods, such as K-means clustering and hierarchical clustering, base gene clustering directly on the observed measurements. A new model-based clustering method, the clustering of regression models (CORM) method, bases the clustering of genes on their relationship to covariates. It explicitly models different sources of variations and bases gene clustering solely on the systematic variation. Both being partitional clustering, CORM is closely related to K-means clustering. In this paper, we discuss the relationship between the two clustering methods in terms of both model formulation and implications on other important aspects …

A Robust Measure Of Correlation Between Two Genes On A Microarray, Johanna Hardin

Pomona Faculty Publications and Research

Background

The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing …