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

Curis, Emmanuel; Courtin, Cindie; Geoffroy, Pierre Alexis; Laplanche, Jean-Louis; Saubaméa, Bruno; Marie-Claire, Cynthia.
Bioinformatics; 35(2): 258-265, 2019 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-30010788
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 methods is suited for qRT-PCR and RNA-Seq data analyses, and leads to easy-to-interpret, 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 ΔΔCq 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.Supplementary information: Supplementary data are available at Bioinformatics online.
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