Main logo

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.

User report

tutorial arrow
Vote up tools and offer feedback
Give value to tools and make your expertise visible
Give your feedback on this tool
Sign up for free to join and share with the community

0 user reviews

star_border star_border star_border star_border star_border
star star star star star

0 user reviews

star_border star_border star_border star_border star_border
star star star star star

No review has been posted.

RPA forum

tutorial arrow
Communicate with other users
Participate in the forum to get support for using tools. Ask questions about technical specifications.
Take part in the discussion
Sign up for free to ask question and share your advices

No open topic.

RPA classification

RPA specifications

Unique identifier:
Software type:
Restrictions to use:
Programming languages:
Computer skills:
Robust Probabilistic Averaging
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
BSD 2-clause “Simplified” License
affy, BiocGenerics, methods, phyloseq

RPA distribution


tutorial arrow
Upload and version your source code
Get a DOI for each update to improve tool traceability. Archive your releases so the community can easily visualize progress on your work.
Facilitate your tool traceability
Sign up for free to upload your code and get a DOI

No versioning.


RPA support



tutorial arrow
Promote your skills
Define all the tasks you managed and assign your profile the appropriate badges. Become an active member.
Promote your work
Sign up for free to badge your contributorship



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).

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.