Robust Probabilistic Averaging (RPA)

Robust Probabilistic Averaging (RPA)

Robust Probabilistic Averaging (RPA) is a fully scalable algorithm for probe-level preprocessing and analysis of short oligonucleotide microarray collections of any size, from moderately sized standard data sets to arbitrarily large microarray atlases involving tens of thousands of samples, or more.

Special wrappers are available for Affymetrix gene expression arrays and phylogenetic micoarrays (HITChip).

RPA also provides explicit data-driven estimates of probe-specific affinity and noise based on a rigorous probabilistic model and significantly outperforms the standard RMA model in benchmarking tests (NAR 2013), at the same time achieving a full scalability.

How to use

The method is available in R/Bioconductor, and documented in the publications listed below. For all installation and usage instructions, kindly see the RPA wiki.

Citing RPA

The RPA methodology has been documented in these two publications. Kindly cite if appropriate:

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RPA documentation built on Nov. 8, 2020, 7:47 p.m.