README.md

In sanchezi/openSilexStatR: Lattice Experiments Data Analysis

openSilexStatR

Package In progress...

A set of statistical functions and rmarkdown scripts to analyze experiments in a lattice platform - OpenSilex system information.

https://sanchezi.github.io/openSilexStatR/

• Spatial representation of a greenhouse or a lattice platform
• Create a video with imageGreenhouse() function. see vignette videoImageGreenhouse
• Detection of outliers in a set of points using the smoothing of a local regression (Loader 2013) and calculating a confidence interval of the prediction. see vignette detectOutlierPoints
• Detection of outliers in a set of points using a bayesian spatio-temporal ANOVA model (Lee, 2018). see vignette CARBayesSTReport
• Detection of outlier time courses using a nonparametric spline (Gu, 2014). see vignette gssAnalysisReport
• Detection of outlier plant, defined as a biological replicate deviating from the overall distribution of plants on a multi-criteria basis, regardless of the quality of measurements. see vignette detectOutlierCurves

The functions of the package are constructed according to a data set structure of a lattice experiment (spatial coordinates) and for some with temporal informations and therefore are not entirely generic.

Please have a look to the structure of the example datasets provided by the package (plant1, plant2, plant3, plant4, PAdata). Mostly, the following columns are required: * the Line and Position columns (coordinates in a greenhouse) * Ref: a unique identifiant * genotypeAlias: genotypes used in the experiment * scenario: scenario applied to the experiment * time: a numeric time variable, for exemple a thermal time

Installation

To install the openSilexStatR package, the easiest is to install it directly from Github. Open an R session and run the following commands:

library(remotes) 
install_github("sanchezi/openSilexStatR",build_vignettes=TRUE)

If you want to install the vignettes included in this library, be aware to have the following softwares on your computer:

• if Windows OS: MikTex (do not forget to add its path to the environment variables PATH, ex: "C:\MIKTEX~1.9\miktex\bin\x64\pdflatex.exe"), Rtools
• if Unix OS: rgdal, rgl, imagemagick

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libgdal-dev

sudo apt install libftgl2 libcgal-dev libglu1-mesa-dev libglu1-mesa-dev libx11-dev libfreetype6-dev

sudo add-apt-repository -y ppa:cran/imagemagick
sudo apt-get update
sudo apt-get install imagemagick-common
sudo apt-get install -y libmagick++-dev

Even if it is less informative, you can also install the package without the vignettes:

library(remotes) 
install_github("sanchezi/openSilexStatR")

Usage

Once the package is installed on your computer, it can be loaded into a R session:

library(openSilexStatR)
help(package="openSilexStatR")

Others useful packages

You can have a look to the following R packages allowing to openSilex users to retrieve data from the openSilex Web Service:

• https://github.com/OpenSILEX/opensilex-ws-client-R
• https://github.com/OpenSILEX/phis-ws-client-r-tool
• https://github.com/OpenSILEX/phisWSClientR

Citation

• Alvarez Prado, S., Sanchez, I., Cabrera Bosquet, L., Grau, A., Welcker, C., Tardieu, F., Hilgert, N. (2019). To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?. Journal of Experimental Botany, 70 (15), 3693-3698. , DOI : 10.1093/jxb/erz191 https://prodinra.inra.fr/record/481355

• Neveu, P., Tireau, A., Hilgert, N., Negre, V., Mineau-Cesari, J., Brichet, N., Chapuis, R., Sanchez, I., Pommier, C., Charnomordic, B., Tardieu, F., Cabrera Bosquet, L. (2019). Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System. New Phytologist, 221 (1), 588-601. , DOI : 10.1111/nph.15385 https://prodinra.inra.fr/record/442308

You should also cite the openSilexStatR package:

citation("openSilexStatR")

See also citation() for citing R itself.

References

1. Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul H.C. Eilers (2018). Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics 23 52 - 71 URL https://doi.org/10.1016/j.spasta.2017.10.003
2. Gu, C. (2013), Smoothing Spline ANOVA Models (2nd Ed). New York: Springer-Verlag.
3. Gu, C. (2014), Smoothing Spline ANOVA Models: R Package gss. Journal of Statistical Software, 58(5), 1-25. URL http://www.jstatsoft.org/v58/i05/.
4. Lee D, Rushworth A, Napier G (2018). Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package. Journal of Statistical Software, 84(9), 1–39. doi: 10.18637/jss.v084.i09.
5. Catherine Loader (2013). locfit: Local Regression, Likelihood and Density Estimation. R package version 1.5-9.1. https://CRAN.R-project.org/package=locfit

sanchezi/openSilexStatR documentation built on Sept. 10, 2020, 1:03 p.m.