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R/data.R
In statgenHTP: High Throughput Phenotyping (HTP) Data Analysis
#' Growth chamber data for an Arabidopsis experiment in the Phenovator platform.
#'
#' A dataset containing data from a growth chamber experiment with Arabidopsis
#' in the Phenovator platform (WUR, Netherlands, Flood et al. 2016). It consists
#' of one experiment with 1,440 plants grown in a growth chamber. The number of
#' tested genotypes is 192 with 6 to 7 replicates per genotype. Four reference
#' genotypes were also tested with 15 or 30 replicates. The studied trait is
#' the photosystem II efficiency (EffpsII) extracted from the pictures over
#' time (van Rooijen et al. 2017). This dataset was kindly provided by René Boesten
#' and Mark Aarts.
#'
#' @format A data.frame with 103,839 rows and 10 columns:
#' \describe{
#' \item{Genotype}{Genotypes}
#' \item{Basin}{Table of experiment}
#' \item{Replicate}{Block define after sowing for post-blocking. They are not
#' full-resolvable blocks.}
#' \item{Image_pos}{Position of the camera}
#' \item{x}{Row coordinate}
#' \item{y}{Column coordinate}
#' \item{Sowing_Position}{Unique pot ID}
#' \item{timepoints}{time of picture}
#' \item{EffpsII}{Efficiency of the photosystem II}
#' \item{pos}{Unique pot ID using rowcol coordinates}
#' }
#'
#'
#' @references Rooijen, Roxanne van, Willem Kruijer, René Boesten,
#' Fred A. van Eeuwijk, Jeremy Harbinson, and Mark G. M. Aarts. 2017.
#' “Natural Variation of YELLOW SEEDLING1 Affects Photosynthetic Acclimation
#' of Arabidopsis Thaliana.” Nature Communications 8 (1).
#' \doi{10.1038/s41467-017-01576-3}
#'
#' Flood, Pádraic J., Willem Kruijer, Sabine K. Schnabel, Rob van der Schoor,
#' Henk Jalink, Jan F. H. Snel, Jeremy Harbinson, and Mark G. M. Aarts. 2016.
#' “Phenomics for Photosynthesis, Growth and Reflectance in Arabidopsis
#' Thaliana Reveals Circadian and Long-Term Fluctuations in Heritability.”
#' Plant Methods 12 (1): 14.
#' \doi{10.1186/s13007-016-0113-y}
"PhenovatorDat1"
#' Arabidopsis data corrected for spatial trends.
#'
#' This dataset contains the corrected data obtained by (1) removing outliers
#' for single observations and (2) running a spatial model on the PhenovatorDat1
#' dataset. See the vignettes for details.
#'
#' @format A data.frame with 103,801 rows and 11 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{EffpsII_corr}{Efficiency of the photosystem II, corrected data}
#' \item{EffpsII}{Efficiency of the photosystem II, raw data}
#' \item{genotype}{Genotypes}
#' \item{repId}{Block define after sowing for post-blocking.}
#' \item{Image_pos}{Position of the camera}
#' \item{check}{Status of the genotypes: check for the reference genotypes,
#' noCheck for the others.}
#' \item{colId}{Column coordinate}
#' \item{rowId}{Row coordinate}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' }
"spatCorrectedVator"
#' Greenhouse data for a maize experiment in the PhenoArch platform.
#'
#' A dataset containing data from a greenhouse experiment with maize in the
#' Phenoarch platform (INRAE, France, Cabrera-Bosquet et al. 2016). It consists
#' of one experiment with 1,671 plants grown in a greenhouse under two water
#' scenarios, well-watered (WW) and water deficit (WD). There are two
#' populations of genotypes, panel 1 and panel 2. Panel 1 contains 60 genotypes
#' with 14 replicates: 7 in WW and 7 in WD. Panel 2 contains 30
#' genotypes with 8 replicates, 4 in WW and 4 in WD. The studied trait is the
#' leaf area extracted from the pictures over time (LeafArea).
#' Plants were pictured every day for 33 days. This dataset was kindly
#' provided by Llorenç Cabrera-Bosquet and Claude Welcker.
#'
#' @format A data.frame with 42,536 rows and 14 columns:
#' \describe{
#' \item{Date}{Date of measurement}
#' \item{pos}{Unique pot using rowcol coordinate}
#' \item{Genotype}{Genotype}
#' \item{Scenario}{Water regime, WW or WD}
#' \item{population}{Panel 1 or 2}
#' \item{Row}{Pot position on the conveyor belt (i.e. row coordinate)}
#' \item{Col}{Line of conveyor belt (i.e. column coordinate)}
#' \item{Biomass}{Biomass from the picture}
#' \item{LeafArea}{Leaf area from the picture}
#' \item{PlantHeight}{Plant height from the picture}
#' \item{LeafCount}{Number of leaves manually scored}
#' \item{phyllocron}{Leaf emission rate}
#' }
#'
#'
#' @references Cabrera-Bosquet, Llorenç, Fournier Christian, Brichet Nicolas,
#' Welcker Claude, Suard Benoît, and Tardieu François. 2016.
#' “High-throughput estimation of incident light, light interception and
#' radiation-use efficiency of thousands of plants in a phenotyping platform.”
#' New Phytologist 212 (1): 269-81.
#' \doi{10.1111/nph.14027}
"PhenoarchDat1"
#' Maize data corrected for spatial trends.
#'
#' This dataset contains the corrected data obtained by (1) removing outliers
#' for single observations and (2) running a spatial model on the
#' PhenoarchDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 37,038 rows and 9 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{LeafArea_corr}{Leaf area, corrected data}
#' \item{LeafArea}{Leaf area from the picture, raw data}
#' \item{wt}{Weight factor}
#' \item{genotype}{Genotypes}
#' \item{geno.decomp}{Combination of treatment levels to decompose the
#' genotypic variance (see vignettes)}
#' \item{colId}{Column coordinate}
#' \item{rowId}{Row coordinate}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' }
"spatCorrectedArch"
#' Maize data, genotypic predictions.
#'
#' This dataset contains the genotypic predictions obtained by (1) removing
#' outliers for single observations and (2) running a spatial model on the
#' PhenoarchDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 6,120 rows and 6 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{geno.decomp}{Combination of treatment levels to decompose the
#' genotypic variance (see vignettes)}
#' \item{genotype}{Genotypes}
#' \item{predicted.values}{Biomass, predicted values}
#' \item{standard.errors}{Standard errors associated with the prediction}
#' }
"spatPredArch"
#' Greenhouse data for an experiment in the RootPhAir platform.
#'
#' A dataset containing greenhouse data from the RootPhAir platform
#' (UCLouvain, Belgium). It consists of one experiment with one
#' aeroponic tanks with 340 maize plants. The studied traits are the root tip
#' coordinates in y and x axis, extracted from the pictures over time.
#' Plants were pictured every 2 hours for 10 days.
#' This dataset was kindly provided by Xavier Draye.
#'
#' @format A data.frame with 16,275 rows and 10 columns:
#' \describe{
#' \item{Exp}{Experiment number}
#' \item{thermalTime}{Thermal time cumulated}
#' \item{Genotype}{Genotype}
#' \item{plantId}{Unique pot using tank and rowcol coordinate}
#' \item{Tank}{Tank A or B}
#' \item{Strip}{Number of strip of five plants (i.e. row coordinate)}
#' \item{Pos}{Position within th strip (i.e. column coordinate)}
#' \item{tipPos_x}{Position of the root tip in x axis}
#' \item{tipPos_y}{Position of the root tip in y axis}
#' \item{Time}{Time of measurement}
#' }
"RootDat1"
#' Root data corrected for outliers for single observations.
#'
#' This dataset contains the corrected data obtained by removing outliers for
#' single observations on the RootDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 15,934 rows and 8 columns:
#' \describe{
#' \item{timePoint}{Original time points, date and time}
#' \item{Date}{Date}
#' \item{thermalTime}{Thermal time cumulated}
#' \item{Exp}{Experiment number}
#' \item{genotype}{Genotypes}
#' \item{Tank}{Tank in the greenhouse}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' \item{rowId}{Row coordinate}
#' \item{colId}{Column coordinate}
#' \item{tipPos_y}{Position of the root tip in y axis}
#' }
"noCorrectedRoot"
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#' Growth chamber data for an Arabidopsis experiment in the Phenovator platform.
#'
#' A dataset containing data from a growth chamber experiment with Arabidopsis
#' in the Phenovator platform (WUR, Netherlands, Flood et al. 2016). It consists
#' of one experiment with 1,440 plants grown in a growth chamber. The number of
#' tested genotypes is 192 with 6 to 7 replicates per genotype. Four reference
#' genotypes were also tested with 15 or 30 replicates. The studied trait is
#' the photosystem II efficiency (EffpsII) extracted from the pictures over
#' time (van Rooijen et al. 2017). This dataset was kindly provided by René Boesten
#' and Mark Aarts.
#'
#' @format A data.frame with 103,839 rows and 10 columns:
#' \describe{
#' \item{Genotype}{Genotypes}
#' \item{Basin}{Table of experiment}
#' \item{Replicate}{Block define after sowing for post-blocking. They are not
#' full-resolvable blocks.}
#' \item{Image_pos}{Position of the camera}
#' \item{x}{Row coordinate}
#' \item{y}{Column coordinate}
#' \item{Sowing_Position}{Unique pot ID}
#' \item{timepoints}{time of picture}
#' \item{EffpsII}{Efficiency of the photosystem II}
#' \item{pos}{Unique pot ID using rowcol coordinates}
#' }
#'
#'
#' @references Rooijen, Roxanne van, Willem Kruijer, René Boesten,
#' Fred A. van Eeuwijk, Jeremy Harbinson, and Mark G. M. Aarts. 2017.
#' “Natural Variation of YELLOW SEEDLING1 Affects Photosynthetic Acclimation
#' of Arabidopsis Thaliana.” Nature Communications 8 (1).
#' \doi{10.1038/s41467-017-01576-3}
#'
#' Flood, Pádraic J., Willem Kruijer, Sabine K. Schnabel, Rob van der Schoor,
#' Henk Jalink, Jan F. H. Snel, Jeremy Harbinson, and Mark G. M. Aarts. 2016.
#' “Phenomics for Photosynthesis, Growth and Reflectance in Arabidopsis
#' Thaliana Reveals Circadian and Long-Term Fluctuations in Heritability.”
#' Plant Methods 12 (1): 14.
#' \doi{10.1186/s13007-016-0113-y}
"PhenovatorDat1"
#' Arabidopsis data corrected for spatial trends.
#'
#' This dataset contains the corrected data obtained by (1) removing outliers
#' for single observations and (2) running a spatial model on the PhenovatorDat1
#' dataset. See the vignettes for details.
#'
#' @format A data.frame with 103,801 rows and 11 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{EffpsII_corr}{Efficiency of the photosystem II, corrected data}
#' \item{EffpsII}{Efficiency of the photosystem II, raw data}
#' \item{genotype}{Genotypes}
#' \item{repId}{Block define after sowing for post-blocking.}
#' \item{Image_pos}{Position of the camera}
#' \item{check}{Status of the genotypes: check for the reference genotypes,
#' noCheck for the others.}
#' \item{colId}{Column coordinate}
#' \item{rowId}{Row coordinate}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' }
"spatCorrectedVator"
#' Greenhouse data for a maize experiment in the PhenoArch platform.
#'
#' A dataset containing data from a greenhouse experiment with maize in the
#' Phenoarch platform (INRAE, France, Cabrera-Bosquet et al. 2016). It consists
#' of one experiment with 1,671 plants grown in a greenhouse under two water
#' scenarios, well-watered (WW) and water deficit (WD). There are two
#' populations of genotypes, panel 1 and panel 2. Panel 1 contains 60 genotypes
#' with 14 replicates: 7 in WW and 7 in WD. Panel 2 contains 30
#' genotypes with 8 replicates, 4 in WW and 4 in WD. The studied trait is the
#' leaf area extracted from the pictures over time (LeafArea).
#' Plants were pictured every day for 33 days. This dataset was kindly
#' provided by Llorenç Cabrera-Bosquet and Claude Welcker.
#'
#' @format A data.frame with 42,536 rows and 14 columns:
#' \describe{
#' \item{Date}{Date of measurement}
#' \item{pos}{Unique pot using rowcol coordinate}
#' \item{Genotype}{Genotype}
#' \item{Scenario}{Water regime, WW or WD}
#' \item{population}{Panel 1 or 2}
#' \item{Row}{Pot position on the conveyor belt (i.e. row coordinate)}
#' \item{Col}{Line of conveyor belt (i.e. column coordinate)}
#' \item{Biomass}{Biomass from the picture}
#' \item{LeafArea}{Leaf area from the picture}
#' \item{PlantHeight}{Plant height from the picture}
#' \item{LeafCount}{Number of leaves manually scored}
#' \item{phyllocron}{Leaf emission rate}
#' }
#'
#'
#' @references Cabrera-Bosquet, Llorenç, Fournier Christian, Brichet Nicolas,
#' Welcker Claude, Suard Benoît, and Tardieu François. 2016.
#' “High-throughput estimation of incident light, light interception and
#' radiation-use efficiency of thousands of plants in a phenotyping platform.”
#' New Phytologist 212 (1): 269-81.
#' \doi{10.1111/nph.14027}
"PhenoarchDat1"
#' Maize data corrected for spatial trends.
#'
#' This dataset contains the corrected data obtained by (1) removing outliers
#' for single observations and (2) running a spatial model on the
#' PhenoarchDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 37,038 rows and 9 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{LeafArea_corr}{Leaf area, corrected data}
#' \item{LeafArea}{Leaf area from the picture, raw data}
#' \item{wt}{Weight factor}
#' \item{genotype}{Genotypes}
#' \item{geno.decomp}{Combination of treatment levels to decompose the
#' genotypic variance (see vignettes)}
#' \item{colId}{Column coordinate}
#' \item{rowId}{Row coordinate}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' }
"spatCorrectedArch"
#' Maize data, genotypic predictions.
#'
#' This dataset contains the genotypic predictions obtained by (1) removing
#' outliers for single observations and (2) running a spatial model on the
#' PhenoarchDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 6,120 rows and 6 columns:
#' \describe{
#' \item{timeNumber}{Time number obtained after formatting the original
#' dataset with the function createTP.}
#' \item{timePoint}{Original time point.}
#' \item{geno.decomp}{Combination of treatment levels to decompose the
#' genotypic variance (see vignettes)}
#' \item{genotype}{Genotypes}
#' \item{predicted.values}{Biomass, predicted values}
#' \item{standard.errors}{Standard errors associated with the prediction}
#' }
"spatPredArch"
#' Greenhouse data for an experiment in the RootPhAir platform.
#'
#' A dataset containing greenhouse data from the RootPhAir platform
#' (UCLouvain, Belgium). It consists of one experiment with one
#' aeroponic tanks with 340 maize plants. The studied traits are the root tip
#' coordinates in y and x axis, extracted from the pictures over time.
#' Plants were pictured every 2 hours for 10 days.
#' This dataset was kindly provided by Xavier Draye.
#'
#' @format A data.frame with 16,275 rows and 10 columns:
#' \describe{
#' \item{Exp}{Experiment number}
#' \item{thermalTime}{Thermal time cumulated}
#' \item{Genotype}{Genotype}
#' \item{plantId}{Unique pot using tank and rowcol coordinate}
#' \item{Tank}{Tank A or B}
#' \item{Strip}{Number of strip of five plants (i.e. row coordinate)}
#' \item{Pos}{Position within th strip (i.e. column coordinate)}
#' \item{tipPos_x}{Position of the root tip in x axis}
#' \item{tipPos_y}{Position of the root tip in y axis}
#' \item{Time}{Time of measurement}
#' }
"RootDat1"
#' Root data corrected for outliers for single observations.
#'
#' This dataset contains the corrected data obtained by removing outliers for
#' single observations on the RootDat1 dataset. See the vignettes for details.
#'
#' @format A data.frame with 15,934 rows and 8 columns:
#' \describe{
#' \item{timePoint}{Original time points, date and time}
#' \item{Date}{Date}
#' \item{thermalTime}{Thermal time cumulated}
#' \item{Exp}{Experiment number}
#' \item{genotype}{Genotypes}
#' \item{Tank}{Tank in the greenhouse}
#' \item{plotId}{Unique pot ID using rowcol coordinates}
#' \item{rowId}{Row coordinate}
#' \item{colId}{Column coordinate}
#' \item{tipPos_y}{Position of the root tip in y axis}
#' }
"noCorrectedRoot"
Try the statgenHTP package in your browser
Any scripts or data that you put into this service are public.
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