Abstract
BackgroundA novel method of microarray preprocessing - Frozen Robust Multi-array Analysis (fRMA) - has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advantages of multi-array preprocessing methods. The frozen parameter estimates required by this algorithm are generated using a large database of publicly available arrays. Curation of such a database and creation of the frozen parameter estimates is time-consuming; therefore, fRMA has only been implemented on the most widely used Affymetrix platforms.ResultsWe present an R package, frmaTools, that allows the user to quickly create his or her own frozen parameter vectors. We describe how this package fits into a preprocessing workflow and explore the size of the training dataset needed to generate reliable frozen parameter estimates. This is followed by a discussion of specific situations in which one might wish to create one's own fRMA implementation. For a few specific scenarios, we demonstrate that fRMA performs well even when a large database of arrays in unavailable.ConclusionsBy allowing the user to easily create his or her own fRMA implementation, the frmaTools package greatly increases the applicability of the fRMA algorithm. The frmaTools package is freely available as part of the Bioconductor project.
Highlights
A novel method of microarray preprocessing - Frozen Robust Multi-array Analysis - has recently been developed
3.1 Training Data The frozen parameter vectors used in the Frozen Robust Multiarray Analysis (fRMA) algorithm are generated using a training dataset comprised of a balanced sample from multiple batches
Some probes may only perform poorly in a small handful of tissues or laboratory conditions, so it is advantageous to include as many different tissues and experiments as possible to maximize our chance of detecting unreliable probes
Summary
We present an R package, frmaTools, that allows the user to quickly create his or her own frozen parameter vectors. We describe how this package fits into a preprocessing workflow and explore the size of the training dataset needed to generate reliable frozen parameter estimates. This is followed by a discussion of specific situations in which one might wish to create one’s own fRMA implementation. For a few specific scenarios, we demonstrate that fRMA performs well even when a large database of arrays in unavailable
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