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Plant growth is usually modeled by transitions between different development phases affecting simultaneously various phenotypes. While some phenotypic covariates, such as the number of leaves, can be repeatedly observed on the same plant, others require destructive observations; a question which raises a great attention is to predict the latter from repeated observations of the former. In particular, floral transition occurring inside the stem, which is a key-step in the plant lifecycle, is indirectly measured by plant dissections which indicate whether the transition already occurred; this statistical context corresponds to current status data, a specific censoring scheme in survival analysis. Nevertheless, in crop plant experiments, censoring is usually circumvented by evaluating the average floral transition time on a plot of plants, ignoring the plant level variability. The goal of this project is to develop survival analysis methods in the current status data framework to analyse floral transition at the plant level. Survival analysis models are mostly developed in the biomedical field, but crop plant experiments present specificities in terms of design, nature of the covariates and problematics which call for adapted methods. The aim of my project is to develop joint models adapted to current status of floral transition time and leaf appearance process, to analyse their relevance on data of the ITEMAIZE project headed by geneticists at INRA, in which observations of maize plants are conducted every year, and furthermore to gather these models with Bayesian experimental design framework to determine the optimal design for data collection.
After a higher education in mathematics, I completed a PhD in theoretical statistics in which I developed non-parametric methods in various regression and survival analysis frameworks. Then I chose to move to applications and after a post-doctoral experience in molecular epidemiology at the University of Tromso (Norway), I was recruited as a young permanent researcher (CR2) in the mathematics department of INRA in September 2013. At INRA, I developed collaborations with several teams of biologists, in particular in omics and meta-omics data analysis, using diversified large dimension methods. I recently enlarged my topics of applications to the domain of plant dynamics, through the ITEMAIZE project which aims at investigating the environmental and genetic determinants of maize flowering time, and raises survival analysis questions.
Raguideau, S, Plancade, S, Pons, N., Leclerc, M. Laroche., 2016. Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-Negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota, B. PLOS Comput. Bio, 12, 1-29.
Mach, N., Plancade, S., Pacholewska, A., Lecardonnel, J., Rivière, J., Moroldo, M., Vaiman, A., Morgenthaler, C., Beinat, M., Névot, A., Robert, C., Barrey, E., 2016. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse, Nature Scientific Reports, 6, 22932. Doi: 10.1038/ srep.22932.
Lund E., Holden L., Bøvelstad H., Plancade S., Mode N., Günther CC., Nuel G., Thalabard JJ., Holden M., 2016. A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle. BMC Medical Research Methodology. Doi: 10.1186/s12874-016-0129-z.
Plancade, S., 2013. Adaptive estimation of the conditional cumulative distribution function from current status data, J. Statist. Plann. Inference, 143(9), 1466-1485.