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Provides tools for using consistent color schemes in diagnostic and other plots. Also defines the Processor and Pipeline classes used so objects can maintain a history of how they were produced. Class comparison problems start with two or more known groups of samples, and ask the analyst to find genes or proteins that are different in some way between the two groups.
The Tail Rank test is a new method we have developed for finding biomarkers in microarray or proteomics data sets. The method is essentially non-parametric, focusing on the tails of the distributions in the two classes being compared.
The method allows analysts to perform realistic sample size and power computations. GenAlgo is a package that implements a genetic algorithm, with a specific focus on performing feature selection from omics datasets to develop predicative models.
Umpire is a package that implements tools for simulating realistic gene expression data based on a variety of biological principles. SuperCurve is a package we have developed to analyze reverse-phase protein arrays. The package includes routines to load raw data files quantified by MicroVigene, to fit a four-parameter joint logistic model in order to estimate protein concentrations, along with methods to assess the quality of the fit.
[PDF] Gene expression analysis with the parametric bootstrap. - Semantic Scholar
SlideDesignerGUI is a graphical tool to allow researchers to describe the location and concentration of different positive and negative controls on a reverse phase protein array.. In this paper, we propose the use of a deterministic rule, applied to the parameters of the gene expression distribution, to select a target subset of genes that are of biological interest.
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Van Der Laan , Jennifer F. Venue: Biostatistics Citations: 36 - 7 self. Abstract: In this dissertation, we develop novel statistical and computational methods for differential expression analysis in high-throughput gene expression data.
Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates
In the first part, we develop statistical models for differential expression with a variety of study designs. In project one, we present an efficient algorithm for the detection of differential expression and splicing of genes in RNA-Seq data.
Our approach considers three cases for each gene: no differential expression, differential expression without differential splicing, and differential splicing. We use a Poisson regression framework to model the read counts and a hierarchical likelihood ratio test for model selection. In project two, we present a non-parametric approach for the joint detection of differential expression and splicing of genes by introducing a new statistic named gene-level differential score and using a permutation test to assess the statistical significance.