Microarray_analysis

Microarray_analysis - Microarray data analysis Jeremy...

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Microarray data analysis Jeremy Glasner Genetics 875 November 29, 2007
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Why cluster data? TMI- can’t “see” patterns in data Reduce complexity in data sets Allow “visualization” of complex data
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Preliminary questions you need to ask before you start clustering What genes and experiments to cluster? What normalization, standardization, or transformation should be applied to data? What distance function should be used? What clustering method should be used?
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Cluster differentially expressed genes or all genes? Determine which changes are significant: Fixed cutoff (fold-change>4) Replication allows assessment of variability Common statistics such as the t-test are often used for gene expression data. Significance of the value is then determined by referring to the t distribution. This assumes that the data is normally distributed, which may not be true. Gene expression experiments may require thousands of statistical tests and significance should be adjusted to reflect this. A standard Bonferroni correction is the p-value multiplied by the number of tests but is likely too conservative.
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Simple scatterplot for two experiments
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Principle Components Analysis (PCA, a.k.a. SVD) Definition : Principle Components - A set of variables that define a projection that encapsulates the maximum amount of variation in a dataset and is orthogonal (and therefore uncorrelated) to the previous principle component of the same dataset.
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This note was uploaded on 08/08/2008 for the course GEN 875 taught by Professor Glasner during the Fall '07 term at Wisconsin.

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Microarray_analysis - Microarray data analysis Jeremy...

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