A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database |
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Authors: | Simon Katz Rafael A Irizarry Xue Lin Mark Tripputi and Mark W Porter |
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Institution: | (1) Gene Logic Inc, 610 Professional Dr, Gaithersburg, MD 20876, USA;(2) Department of Biostatistics, Johns Hopkins Bloomberg School of Health, 615 N Wolfe St, Baltimore, MD 21205, USA;(3) Department of Applied Mathematics and Statistics, Johns Hopkins. University, 302 Whitehead Hall, 3400 North Charles Street, Baltimore, MD 21218, USA |
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Abstract: | Background Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously
analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated
with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used
for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that
an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing
algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing
of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern
computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training
set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be
done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip
Averaging (RMA) algorithm which we refer to as refRMA. |
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