######################
# SpaTemHTP_pipeline #
######################
#' Pipeline function for HTP data
#'
#' Pipeline function performing different level of data treatment on high
#' throughput phenotyping (HTP) time series data.
#'
#' The function perform different operations to progressively enrich the data
#' in information content. The user can select the amount of treatment he wants
#' to apply on the data by selecting among the following options:
#'
#' \enumerate{
#'
#' \item{}{Raw data with experimetal design information.}
#'
#' \item{}{Raw data with outliers detected (using \code{\link{outliers_det_boxplot}})
#' and experimental design information.}
#'
#' \item{}{Raw data with missing values imputed after outliers detection
#' (using \code{\link{miss_imp_PMM}}) and experimental design information.}
#'
#' \item{}{Genotype adjusted means (BLUEs) time series using the SpATS model for
#' spatial correction after outliers detection and imputation.}
#'
#' \item{}{Selection of an optimal section or time point in the whole genotype
#' BLUEs time series according to an heritability criteria or change point
#' analysis (\code{\link{TS_select}}).}
#'
#' \item{}{Further analysis of the time series fitting a logistic curve to the
#' genotype BLUEs TS.}
#'
#' The two last options (selection on the time series and further modelling of
#' the time series) are conditional on the calculation of the genotype adjusted
#' means time series (option 4).
#'
#' }
#'
#' @param exp_id \code{Character} string indicating the name of the experiment.
#' Default = 'exp_x'.
#'
#' @param trait_id \code{Character} string indicating the name of the trait
#' analyzed. Default = 'trait_i'
#'
#' @param out_loc \code{Character} string indicating the path location where
#' results will be saved. Defaut is the working directory
#'
#' @param exp_des_data \code{data.frame} of dimension (N_genotype * N_replicate)
#' x N_variable containing the experimental design information. It must include:
#' a) a 'genotype' column representing the line phenotyped;
#' b) numeric 'row' and 'col' column representing the row and column informaiton,
#' c) the same row and column information into factor columns named 'row_f' and
#' 'col_f'. Other variables like replicate or block can be introduced to be used
#' in the spatially adjusted mixed model computation. The user must set those
#' extra variable in the correct format (generally factor).
#'
#' @param pheno_data \code{data.frame} of dimension (N_genotype * N_replicate)
#' x N_days containing the measured phenotypic values.
#'
#' @param raw_data \code{Logical} value specifying if the user wants the raw data
#' to be returned. Default = TRUE.
#'
#' @param raw_data_out_det \code{Logical} value specifying if the user wants
#' the raw data after outlier detection to be returned. Default = FALSE.
#'
#' @param raw_data_imput \code{Logical} value specifying if the user wants
#' the raw data after imputation to be returned. Default = FALSE.
#'
#' @param raw_data_out_det_imput \code{Logical} value specifying if the user wants
#' the raw data after outliers detection and imputation to be returned.
#' Default = TRUE.
#'
#' @param G_BLUES_TS \code{Logical} value specifying if the user wants
#' to calculate the genotypes adjusted means (BLUEs) time series after
#' spatial adjustment. Default = TRUE.
#'
#' @param G_BLUES_TS_sel \code{Logical} value specifying if the user wants
#' to select the day with the largest h2 on the genotypes adjusted means (BLUEs)
#' time series after. Default = TRUE.
#'
#' @param G_BLUES_TS_log_curve \code{Logical} value specifying if the user wants
#' to perform a logistic curve fitting on the G-BLUES time series Default = TRUE.
#'
#' @param out_det \code{Logical} value specifying if outlier detection should
#' be performed on the phenotypic data. Default = TRUE.
#'
#' @param miss_imp \code{Logical} value specifying if missing value imputation
#' should be performed on the phenotypic data. Default = TRUE.
#'
#' @param sp_adj \code{Logical} value specifying if a mixed model with spatial
#' adjustment (SpATS model) should be used to calculate the genotype BLUEs.
#' Default = TRUE.
#'
#' @param single_mixed_model \code{Logical} value indicating if a 'single-step'
#' mixed model should be calculated. See Details for more explanations.
#' Default = FALSE.
#'
#' @param out_p_val \code{Numeric} value indicating the signficance threshold
#' for outliers detection. Default = 0.05.
#'
#' @param fixed Optional right hand formula object specifying the fixed effects
#' of the SpATS model. Default = NULL.
#'
#' @param random Optional right hand formula object specifying the random effects
#' of the SpATS model. Default = ~ row_f + col_f.
#'
#' @param spatial \code{Character} string specifying the methode used to calculate
#' the spatial surface. must be one of: 'SAP', 'PSANOVA', 'SAP_if_PSANOVA_fail',
#' 'PSANOVA_if_SAP_fail'.
#'
#' @return
#'
#' For each chosen options, the function will save the produced data in
#' a folder created at the specified location. Will also be added.
#'
#' ... (develop further)
#'
#' @author ICRISAT GEMS team
#'
#' @examples
#'
#' data(SG_PH_data)
#'
#' SG_PH_data$col_f <- factor(SG_PH_data$col)
#' SG_PH_data$row_f <- factor(SG_PH_data$row)
#'
#' SG_PH_data$rep <- factor(SG_PH_data$rep)
#' SG_PH_data$block <- factor(SG_PH_data$block)
#'
#' exp_des_data = SG_PH_data[, c("row", "col", "row_f", "col_f","genotype",
#' "rep", "block")]
#'
#' pheno_data <- SG_PH_data[, 6:28]
#'
#' \dontrun{
#'
#' out_loc <- getwd() # specify a directory where the results will be saved
#'
#' results <- SpaTemHTP_pipeline(exp_id = 'Exp_XX', trait_id = 'trait_1',
#' out_loc = out_loc, exp_des_data = exp_des_data, pheno_data = pheno_data,
#' random = ~ rep + rep:block + row_f + col_f, spatial = 'PSANOVA')
#'
#' }
#'
#' @export
#'
# exp_id = 'exp_x'
# trait_id = 'trait_i'
# out_loc <- 'G:/PhD/Test'
#
# data(SG_PH_data)
#
# SG_PH_data$col_f <- factor(SG_PH_data$col)
# SG_PH_data$row_f <- factor(SG_PH_data$row)
#
# SG_PH_data$rep <- factor(SG_PH_data$rep)
# SG_PH_data$block <- factor(SG_PH_data$block)
#
# exp_des_data = SG_PH_data[, c("row", "col", "row_f", "col_f","genotype",
# "rep", "block")]
#
# pheno_data <- SG_PH_data[, 6:28]
#
# raw_data <- TRUE
# raw_data_out_det <- FALSE
#
# out_det = TRUE
# miss_imp = TRUE
# sp_adj = TRUE
# single_mixed_model = FALSE
# out_p_val = 0.05
# fixed = NULL
# random = ~ row_f + col_f
<- (exp_id = 'exp_x', trait_id = 'trait_i',
out_loc = ,
exp_des_data, pheno_data,
raw_data = ,
raw_data_out_det = ,
raw_data_imput = ,
raw_data_out_det_imput = ,
G_BLUES_TS = ,
G_BLUES_TS_sel = ,
G_BLUES_TS_log_curve = ,
out_det = , miss_imp = , sp_adj = ,
single_mixed_model = , out_p_val = 0.05,
fixed = , random = ~ row_f + col_f,
spatial) {
### Check data format
# is there a genotype column
if (!('genotype' %in% (exp_des_data))){
('There is not column called genotype in exp_des_data.')
}
### Check consistency in the command
(!G_BLUES_TS & G_BLUES_TS_sel){
('To perform the selection of the G-BLUEs time series G_BLUES_TS must be TRUE')
}
(!G_BLUES_TS & G_BLUES_TS_log_curve){
('To perform the logistic curve analysis the option G_BLUES_TS must be TRUE')
}
### create folder to store the results
if ((out_loc)){
out_loc <- () } {
(!(out_loc)){('The ouput file location path is misspecified')}
}
fold_loc <- (out_loc, (exp_id, '_', trait_id))
(fold_loc)
# create an empty list to store the results
res_list <- ()
i <- 1
### Raw data
(raw_data){
raw_data <- (exp_des_data, pheno_data, stringsAsFactors = )
# save the data in .csv file
(x = raw_data, = (fold_loc, 'raw_data.csv'),
= )
# store the data in the list
res_list[i]] <- raw_data
(res_list)[i] <- 'raw_data'
i <- i + 1
# plot data
geno_id <- exp_des_data$genotype
(filename = (fold_loc, 'raw_data_plot.jpeg'), = 960,
= 960)
(data_TS = pheno_data, geno_id = geno_id, rep_av = ,
main = 'TS plot of the replicated genotype data')
()
}
### raw data out detect
(raw_data_out_det){
pheno_data_out <- ( = pheno_data, = )
raw_data_out <- (exp_des_data, pheno_data_out,
stringsAsFactors = )
# save the data in .csv file
(x = raw_data_out, = (fold_loc, 'raw_data_out_det.csv'),
= )
# store the data in the list
res_list[i]] <- raw_data_out
(res_list)[i] <- 'raw_data_out'
i <- i + 1
# plot data
geno_id <- exp_des_data$genotype
(filename = (fold_loc, 'raw_data_out_det_plot.jpeg'), = 960,
= 960)
(data_TS = pheno_data_out, geno_id = geno_id, rep_av = ,
main = 'TS plot of the replicated genotype data after outlier detection')
()
}
### raw data imputation
(raw_data_imput){
pheno_data_imp <- ( = pheno_data, = )
raw_data_imp <- (exp_des_data, pheno_data_imp,
stringsAsFactors = )
# save the data in .csv file
(x = raw_data_imp, = (fold_loc, 'raw_data_imp.csv'),
= )
# store the data in the list
res_list[i]] <- raw_data_imp
(res_list)[i] <- 'raw_data_imp'
i <- i + 1
# plot data
geno_id <- exp_des_data$genotype
(filename = (fold_loc, 'raw_data_imp_plot.jpeg'), = 960,
= 960)
(data_TS = pheno_data_imp, geno_id = geno_id, rep_av = ,
main = 'TS plot of the replicated genotype data after imputation')
()
}
### raw data outliers detection and imputation
(raw_data_out_det_imput){
pheno_data_out <- ( = pheno_data, = )
pheno_data_out_imp <- ( = pheno_data_out, = )
raw_data_out_imp <- (exp_des_data, pheno_data_out_imp,
stringsAsFactors = )
# save the data in .csv file
(x = raw_data_out_imp, = (fold_loc, 'raw_data_out_imp.csv'),
= )
# store the data in the list
res_list[i]] <- raw_data_out_imp
(res_list)[i] <- 'raw_data_out_imp'
i <- i + 1
# plot data
geno_id <- exp_des_data$genotype
(filename = (fold_loc, 'raw_data_out_imp_plot.jpeg'), = 960,
= 960)
(data_TS = pheno_data_out_imp, geno_id = geno_id, rep_av = ,
main = 'TS plot of the replicated genotype data after outliers detection and imputation')
()
}
### G-BLUES time series computation
(G_BLUES_TS){
G_BLUEs <- (exp_des_data, pheno_data,
out_det = out_det, miss_imp = miss_imp,
sp_adj = sp_adj,
single_mixed_model = single_mixed_model,
out_p_val = out_p_val, fixed = fixed,
random = random, spatial = spatial, h2_comp = ,
print_day = , = )
G_BLUES_TS_data <- G_BLUEs$G_BLUES
G_BLUES_stdev <- G_BLUEs_std
h2 <- G_BLUEs$h2
# save the data in .csv file
(x = G_BLUES_TS_data, = (fold_loc, 'G_BLUES_TS_data.csv'))
(x = G_BLUES_stdev, = (fold_loc, 'G_BLUES_stdev.csv'))
(x = h2, = (fold_loc, 'heritability_values.csv'),
= )
# store the data in the list
res_list[i]] <- G_BLUES_TS_data
(res_list)[i] <- 'G_BLUES_TS_data'
i <- i + 1
res_list[i]] <- h2
(res_list)[i] <- 'h2'
i <- i + 1
# plot data
(filename = (fold_loc, 'G_BLUES_TS_data_plot.jpeg'), = 960,
= 960)
(data_TS = G_BLUES_TS_data, h2 = h2,
main = 'Genotype BLUEs TS plot with time point having the highest h2')
()
}
### Selection on the G-BLUEs time series
(G_BLUES_TS_sel){
G_BLUES_val_h2 <- ( = G_BLUES_TS_data, h2 = h2, method = 'h2_opt')
# save the data in .csv file
(x = G_BLUES_val_h2, = (fold_loc, 'G_BLUES_values_highest_h2.csv'),
= )
res_list[i]] <- G_BLUES_val_h2
(res_list)[i] <- 'G_BLUES_val_h2'
i <- i + 1
}
### logistic curve fitting on the G-BLUES time series
(G_BLUES_TS_log_curve){
trend_an <- ( = G_BLUES_TS_data)
G_BLUES_TS_log_val <- trend_an$log_val
# save the data in .csv file
(x = G_BLUES_TS_log_val, = (fold_loc, 'G_BLUES_TS_log_val.csv'),
= )
# store the data in the list
res_list[i]] <- G_BLUES_TS_log_val
(res_list)[i] <- 'G_BLUES_TS_log_val'
i <- i + 1
# plot data
(filename = (fold_loc, 'G_BLUES_TS_log_val_curve_plot.jpeg'), = 960,
= 960)
(data_TS = G_BLUES_TS_log_val, main = 'G-BLUEs logistic curve fit plot')
()
####
}
(res_list, = (fold_loc, 'res_list.RData'))
(res_list)
}
