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Jean-Louis Laplanche

dblp:233/5003 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-2836-2903ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 67% Computational science and engineering · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering
compositional data analysis
0.412019
Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios · Bioinform. 2019
Bioinformatics and computational biology › gene expression analysis
gene co-expression analysis
0.412019
Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios · Bioinform. 2019
Bioinformatics and computational biology
gene expression analysis
0.412019
Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

pairwise expression ratio · 0.4graph analysis · 0.4
YearPublicationVenuePosition
2019 Determination of sets of covariating gene expression using graph analysis on pairwise expression ratios
abstract
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.
Emmanuel Curis, Cindie Courtin, Pierre Alexis Geoffroy, Jean-Louis Laplanche, Bruno Saubaméa, Cynthia Marie-Claire
Bioinform.4