Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios

Emmanuel Curis, Cindie Courtin, Pierre Alexis Geoffroy, Jean-Louis Laplanche, Bruno Saubaméa, Cynthia Marie-Claire, Bonnie Berger
2018 Bioinformatics  
Motivation: RNA quantification experiments result in compositional data, however usual methods for compositional data analysis [additive log ratio (alr), centered log ratio (clr), isometric log ratio (ilr)] do not apply easily and give results difficult to interpret. To handle this, a method based on disjoint subgraphs in a graph whose nodes are the quantified RNAs is proposed. Edges in the graph are defined by lack of change in ratios of the corresponding RNAs between conditions. Results: The
more » ... ions. Results: The methods is suited for qRT-PCR and RNA-Seq data analyses, and leads to easy-tointerpret, graphical results and the identification of set of genes that share a similar behavior when the studied condition changes. For qRT-PCR data, it has better statistical properties than the common DDC q method. Availability and implementation: Construction of all pairwise ratio analysis P-values matrix, and conversion into a graph was implemented in an R package, named SARP.compo. It is freely available for download on the CRAN repository. Example R script using the package are provided as Supplementary Material; the R package includes the data needed. One of these scripts reproduces the Figure 2 of this paper.
doi:10.1093/bioinformatics/bty629 pmid:30010788 fatcat:z4ctauggxna4bhqyh6qibt54l4