RoDEO: Robust Differential Gene Expression       


RoDEO: Robust Differential Gene Expression - overview

RoDEO* is a framework for detecting differentially expressed genes and stable genes between RNA-seq experiments. [*Robust DE Operator]

For detecting differentially expressed genes, we use a normalization that is not based on the relative values of the gene expression but on the relative order of the expression within a sample. Indeed, the expression values of all genes in an experiment are utilized in a re-sampling approach, to tease out robust relative ranks of the genes in several re-generated instances of the RNA-seq experiments.

Robust scale-free measure of expression

Instead of directly working with the expression value of g, we define a character function φ for each gene g. The two most desirable properties of this function are (i) it depends on the expression values of all the other genes in the assay and and (ii) it is scale invariant.

The character function values are estimated by the rodeoexamplere-sampling and binning the genes into a robust sets of P ranks per sample (e.g. P=20). The character function values of a gene g in experiments A and B are compared to determine whether the gene is differentially expressed.

Performance and Application

RoDEO outperforms existing differential expression detectors on benchmark datasets [1].

RoDEO was applied on reed canary grass (Phalaris arundinacea) cultivars' RNA-seq data to detect differentially expressed and stable genes during salt stress [1].

RoDEO has also been applied on metagenomics datasets to detect differentially abundant micro-organisms between samples.


The RoDEO software  is available for non-commercial use.


  1. Niina Haiminen, Manfred Klaas, Zeyu Zhou, Filippo Utro, Paul Cormican, Thomas Didion, Christian Sig Jensen, Chris Mason, Susanne Barth, Laxmi Parida: Comparative Exomics of Phalaris cultivars under salt stress. BMC Genomics 15(Suppl 6):S18, 2014. 
  2. Anna Paola Carrieri, Niina Haiminen, Laxmi Parida: Host phenotype prediction from differentially abundant microbes using RoDEO. Lecture Notes in Computer Science 10477, pp. 27-41, Springer, 2017.