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Articles 1 - 4 of 4
Full-Text Articles in Microarrays
On The Synthesis Of Microarray Experiments, Robert Gentleman, Markus Ruschhaupt, Wolfgang Huber
On The Synthesis Of Microarray Experiments, Robert Gentleman, Markus Ruschhaupt, Wolfgang Huber
Bioconductor Project Working Papers
With many different investigators studying the same disease and with a strong commitment to publish supporting data in the scientific community, there are often many different datasets available for any given disease. Hence there is substantial interest in finding methods for combining these datasets to provide better and more detailed understanding of the underlying biology. We consider the synthesis of different microarray data sets using a random effects paradigm and demonstrate how relatively standard statistical approaches yield good results. We identify a number of important and substantive areas which require further investigation.
Differential Expression With The Bioconductor Project, Anja Von Heydebreck, Wolfgang Huber, Robert Gentleman
Differential Expression With The Bioconductor Project, Anja Von Heydebreck, Wolfgang Huber, Robert Gentleman
Bioconductor Project Working Papers
A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes expression levels. Possible strategies are to do univariate gene-by-gene analysis, and to perform data-driven nonspecific filtering of genes before the actual statistical analysis. …
Classification Using Generalized Partial Least Squares, Beiying Ding, Robert Gentleman
Classification Using Generalized Partial Least Squares, Beiying Ding, Robert Gentleman
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 …
Error Models For Microarray Intensities, Wolfgang Huber, Anja Von Heydebreck, Martin Vingron
Error Models For Microarray Intensities, Wolfgang Huber, Anja Von Heydebreck, Martin Vingron
Bioconductor Project Working Papers
We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation.