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Robust Probabilistic Averaging RPA

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Analyzes the reliability of individual probes directly from gene expression data. A major advantage of the proposed approach is its capability to detect unreliable probes independently of physical models or external, constantly updated information such as genomic sequence data. RPA can be useful in many applications, including evaluation of the end results of gene expression analysis, and recognition of potentially unknown probe-level error sources. It can be also used to quantify the uncertainty in the measurements and in designing the probes, and is also utilized by our model to provide robust estimates of differential gene expression.

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RPA classification

RPA specifications

Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
BSD 2-clause “Simplified” License
Version:
1.31.0
Requirements:
affy, BiocGenerics, methods, phyloseq

RPA distribution

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RPA support

Documentation

Credits

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Publications

Institution(s)

Department of Veterinary Bioscience, University of Helsinki, University of Helsinki, Finland; Laboratory of Microbiology, Wageningen University, Wageningen, Netherlands; European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK; Department of Material Science and Engineering, Universidad Carlos III de Madrid, Leganés, Spain; Department of Mathematics and Statistics, University of Turku, Turku, Finland; Turku Centre for Biotechnology, Turku, Finland

Funding source(s)

This work was supported by Academy of Finland (256950, 127575, 218591), Alfred Kordelin foundation, Ramón Areces Foundation, European Community’s Seventh Framework Programme (FP7/2007-2013), ENGAGE Consortium (HEALTH-F4-2007-201413).

Link to literature

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