The ability to measure gene expression based on a single microarray hybridization is necessary for microarrays to be a useful clinical tool. In its simplest form, this amounts to estimating whether or not each gene is expressed in a given sample. Surprisingly, this problem is quite challenging and has been given relatively little attention for the most part in favor of estimating relative expression.
We propose addressing this problem in three steps. First, we develop a method of assessing the performance of microarray preprocessing methods independent of the microarray technology used. Second, we develop a preprocessing algorithm, frozen RMA (fRMA), that allows one to analyze microarrays individually. Specifically, estimates of probe-specific effects and variances are precomputed and frozen. Then, with new data sets, these are used in concert with information from the new array(s) to normalize and summarize the data. Third, we purpose using the distribution of log2 gene intensities across a wide variety of tissues to estimate an expressed and an unexpressed distribution for each gene, and then for each gene in a sample, denoting it as expressed if its log2 gene intensity is more likely under the expressed distribution than under the unexpressed distribution and as unexpressed otherwise. The output of this algorithm is a vector of ones and zeros denoting which genes are estimated to be expressed (ones) and unexpressed (zeros). We call this a gene expression barcode.
|School:||The Johns Hopkins University|
|School Location:||United States -- Maryland|
|Source:||DAI-B 71/05, Dissertation Abstracts International|
|Subjects:||Biostatistics, Statistics, Bioinformatics|
|Keywords:||Barcoding, Preprocessing, Quality assessment|
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