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Order Restricted Inference for Oscillatory Systems (ORIOS) for Detecting Rhythmic Signals

R code developed by:

Programmed by: Yolanda Larriba, Department of Statistics, University of Valladolid, Valladolid, Spain

Version: ORIOS 1.0 (May 10, 2016)


ORIOS is a model free order restricted inference based algorithm that detects rhythmic components (e.g. transcripts or genes) participating in oscillatory systems such as the circadian clock. Although this software can be used for any oscillatory data, for simplicity of description, throughout this file we shall use the term “circadian clock data” rather than “oscillatory data” and “genes” in place of “components” of an oscillatory system.  

The strength of model free methodology such as the order restricted inference is that, instead of using a mathematical model to describe the shape or pattern of expression, it uses mathematical inequalities to describe patterns.  Thus it is robust and not limited by any mathematical model that may be rigid and restricted. ORIOS not only identifies rhythmic genes, it also classifies them into four typical classes of genes, called cyclical, quasi cyclical, non-flat and non-periodic, and flat, according to its signal shape.  Cyclical and quasi cyclical genes are declared as rhythmic, while non-flat and non-periodic, and flat are declared as non-rhythmic genes. Compared to commonly used rhythmicity detection algorithms in literature (see Hughes et al. (2010) and Thaben et al. (2014)), ORIOS has substantially higher power to detect true rhythmic genes, while also declaring substantially fewer non-rhythmic genes as rhythmic.

Software Info:

Upon downloading the software read the README.pdf for details on how to use the code.


Larriba, Y, Rueda, C, Fernández, MA and Peddada, SD (2016). Order Restricted Inference for Oscillatory Systems for detecting rhythmic signals.



Shyamal Peddada, Ph.D.
Former Chief, Biostatistics & Computational Biology Branch
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