RPA: Probabilistic analysis of probe performance and robust summarization

Motivation: Probe-level models have led to improved performance in microarray studies but the various sources of probe-level contamination are still poorly understood. Data-driven analysis of probe performance can be used to quantify the uncertainty in individual probes and to highlight the relative contribution of dierent noise sources. Improved understanding of the probe-level eects can lead to improved preprocessing techniques and microarray design. Results: We have implemented probabilistic tools for probe performance analysis and summarization on short oligonucleotide arrays. In contrast to standard preprocessing approaches, the methods provide quantitative estimates of probe-specic noise and anity terms and tools to investigate these parameters. Tools to incorporate prior information of the probes in the analysis are provided as well. Comparisons to known probe-level error sources and spike-in data sets validate the approach. Availability: Implementation is freely available in R/BioConductor: http://www.bioconductor.org/packages/release/bioc/html/RPA.html.