#' Estimate Parameters From Real Datasets by ESCO
#'
#' This function is used to estimate useful parameters from a real dataset by
#' using `escoEstimate` function in ESCO package.
#'
#' @param ref_data A count matrix. Each row represents a gene and each column
#' represents a cell.
#' @param verbose Logical.
#' @param other_prior A list with names of certain parameters. Some methods need
#' extra parameters to execute the estimation step, so you must input them. In
#' simulation step, the number of cells, genes, groups, batches, the percent of
#' DEGs are usually customed, so before simulating a dataset you must point it out.
#' See `Details` below for more information.
#' @param seed An integer of a random seed.
#' @return A list contains the estimated parameters and the results of execution
#' detection.
#' @export
#' @details
#' In ESCO, users can input cell group information when it is available but in this case
#' ESCO is not stable and may fail to estimate suitable distribution parameters
#' from real data.
#' If users want to estimate tree structured parameters, set `other_prior = list(tree = TRUE)`.
#' For more instructions, see `Examples`.
#' @references
#' Tian J, Wang J, Roeder K. ESCO: single cell expression simulation incorporating gene co-expression. Bioinformatics, 2021, 37(16): 2374-2381. <https://doi.org/10.1093/bioinformatics/btab116>
#'
#' Github URL: <https://github.com/JINJINT/ESCO>
#'
#' @examples
#' \dontrun{
#' ref_data <- simmethods::data
#'
#' estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
#' other_prior = NULL,
#' verbose = TRUE,
#' seed = 111)
#' If cell group information is available, it can be another prior information.
#' But there is a bug in ESCO, and some datasets can not be estimated due to the
#' failing estimation of distribution parameters.
#' group_condition <- as.numeric(simmethods::group_condition)
#' estimate_result <- simmethods::ESCO_estimation(
#' ref_data = ref_data,
#' other_prior = list(group.condition = group_condition),
#' verbose = TRUE,
#' seed = 111
#' )
#'
#' # ----------------- Estimate tree or trajectory structured data -------------
#' # Load data
#' ref_data <- simmethods::data
#' # Estimate parameters
#' estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
#' other_prior = list(tree = TRUE),
#' verbose = TRUE,
#' seed = 10)
#' }
#'
ESCO_estimation <- function(ref_data,
other_prior = NULL,
verbose = FALSE,
seed
){
##############################################################################
#### Environment ###
##############################################################################
if(!requireNamespace("ESCO", quietly = TRUE)){
message("ESCO is not installed on your device...")
message("Installing ESCO...")
devtools::install_github("JINJINT/ESCO")
}
##############################################################################
#### Check ###
##############################################################################
if(!is.matrix(ref_data)){
ref_data <- as.matrix(ref_data)
}
other_prior[["counts"]] <- ref_data
if(is.null(other_prior[["group.condition"]])){
# other_prior[["group"]] <- FALSE
other_prior[["cellinfo"]] <- NULL
}else{
# other_prior[["group"]] <- TRUE
other_prior[["cellinfo"]] <- other_prior[["group.condition"]]
}
other_prior[["params"]] <- ESCO::newescoParams()
# other_prior[["dirname"]] <- tempdir()
## tree
if(!is.null(other_prior[["tree"]])){
tree <- simutils::make_trees(ref_data = ref_data,
group = other_prior[["cellinfo"]],
is_Newick = FALSE,
is_parenthetic = TRUE,
return_group = TRUE)
group <- tree[["group"]]
tree <- tree[["phyla"]]
}else{
tree <- NULL
}
estimate_formals <- simutils::change_parameters(function_expr = "ESCO::escoEstimate",
other_prior = other_prior,
step = "estimation")
##############################################################################
#### Estimation ###
##############################################################################
if(verbose){
message("Estimating parameters using ESCO")
}
# Seed
set.seed(seed)
# Estimation
estimate_detection <- peakRAM::peakRAM(
estimate_result <- do.call(ESCO::escoEstimate, estimate_formals)
)
if(!is.null(tree)){
estimate_result <- list(estimate_result = estimate_result,
tree = tree,
group = group)
}
##############################################################################
#### Ouput ###
##############################################################################
estimate_output <- list(estimate_result = estimate_result,
estimate_detection = estimate_detection)
return(estimate_output)
}
#' Simulate Datasets by ESCO
#'
#' This function is used to simulate datasets from learned parameters by `escoEstimate`
#' function in ESCO package.
#'
#' @param parameters A object generated by [ESCO::escoEstimate()]
#' @param other_prior A list with names of certain parameters. Some methods need
#' extra parameters to execute the estimation step, so you must input them. In
#' simulation step, the number of cells, genes, groups, batches, the percent of
#' DEGs are usually customed, so before simulating a dataset you must point it out.
#' See `Details` below for more information.
#' @param return_format A character. Alternative choices: list, SingleCellExperiment,
#' Seurat, h5ad. If you select `h5ad`, you will get a path where the .h5ad file saves to.
#' @param verbose Logical. Whether to return messages or not.
#' @param seed A random seed.
#' @export
#' @details
#' In addtion to simulate datasets with default parameters, users want to simulate
#' other kinds of datasets, e.g. a counts matrix with 2 or more cell groups. In
#' ESCO, you can set extra parameters to simulate datasets.
#'
#' The customed parameters you can set are below:
#' 1. nCells. You can directly set `other_prior = list(nCells = 1000)` to simulate 1000 cells.
#' 2. nGenes. You can directly set `other_prior = list(nGenes = 5000)` to simulate 5000 genes.
#' 3. nGroups. You can not directly set `other_prior = list(nGroups = 3)` to simulate 3 groups. Instead, you should set `other_prior = list(prob.group = c(0.2, 0.3, 0.5))` where the sum of group probabilities must equal to 1.
#' 4. de.prob. You can directly set `other_prior = list(de.prob = 0.2)` to simulate DEGs that account for 20 percent of all genes.
#' 5. prob.group. You can directly set `other_prior = list(prob.group = c(0.2, 0.3, 0.5))` to assign three proportions of cell groups. Note that the number of groups always equals to the length of the vector.
#' 6. If users want to simulate tree or trajectory structured data, set `other_prior = list(type = "tree")` or `other_prior = list(type = "traj")`. See `Examples`.
#'
#' For more customed parameters in ESCO, please check [ESCO::escoParams()].
#' @references
#' Tian J, Wang J, Roeder K. ESCO: single cell expression simulation incorporating gene co-expression. Bioinformatics, 2021, 37(16): 2374-2381. <https://doi.org/10.1093/bioinformatics/btab116>
#'
#' Github URL: <https://github.com/JINJINT/ESCO>
#'
#' @examples
#' \dontrun{
#' ## Estimation
#' ref_data <- simmethods::data
#'
#' estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
#' other_prior = NULL,
#' verbose = TRUE,
#' seed = 111)
#' # 1) Simulate with default parameters
#' simulate_result <- simmethods::ESCO_simulation(
#' parameters = estimate_result[["estimate_result"]],
#' other_prior = NULL,
#' return_format = "list",
#' verbose = TRUE,
#' seed = 111
#' )
#'
#' ## counts
#' counts <- simulate_result[["simulate_result"]][["count_data"]]
#' dim(counts)
#' ## cell information
#' col_data <- simulate_result[["simulate_result"]][["col_meta"]]
#' table(col_data$group)
#'
#'
#' # 2) Simulate two groups (20% proportion of DEGs)
#' simulate_result <- simmethods::ESCO_simulation(
#' parameters = estimate_result[["estimate_result"]],
#' other_prior = list(nCells = 1000,
#' nGenes = 2000,
#' de.prob = 0.2,
#' prob.group = c(0.3, 0.7)),
#' return_format = "list",
#' verbose = TRUE,
#' seed = 111
#' )
#'
#' ## counts
#' counts <- simulate_result[["simulate_result"]][["count_data"]]
#' dim(counts)
#' ## cell information
#' col_data <- simulate_result[["simulate_result"]][["col_meta"]]
#' table(col_data$group)/1000
#' ## gene information
#' row_data <- simulate_result[["simulate_result"]][["row_meta"]]
#' table(row_data$de_gene)[2]/2000
#' ### The result of ESCO contains the factors of different groups and uses can
#' ### calculate the fold change by division. For example, the DEFactors of A gene
#' ### in Group1 and Group2 are respectively 2 and 1, and the fold change of A gene
#' ### is 2/1=2 or 1/2=0.5.
#' fc_group1_to_group2 <- row_data$DEFacGroup2/row_data$DEFacGroup1
#' table(fc_group1_to_group2 != 1)[2]/2000 ## de.prob = 0.2
#'
#' # ----------------- Simulate tree or trajectory structured data -------------
#' # Load data
#' ref_data <- simmethods::data
#' # Estimate parameters
#' estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
#' other_prior = list(tree = TRUE),
#' verbose = TRUE,
#' seed = 10)
#'
#' # (1) Simulate tree structured cell groups
#' simulate_result <- simmethods::ESCO_simulation(parameters = estimate_result[["estimate_result"]],
#' other_prior = list(batchCells = 1000,
#' nGenes = 1000,
#' type = "tree"),
#' return_format = "SingleCellExperiment",
#' verbose = TRUE,
#' seed = 111)
#' ## plot
#' result <- scater::logNormCounts(simulate_result[["simulate_result"]])
#' result <- scater::runPCA(result)
#' plotPCA(result, colour_by = "group")
#'
#'
#' # (2) Simulate tree structured cell groups (specify de.prob and group.prob)
#' simulate_result <- simmethods::ESCO_simulation(parameters = estimate_result[["estimate_result"]],
#' other_prior = list(batchCells = 1000,
#' nGenes = 1000,
#' type = "tree",
#' group.prob = c(0.4, 0.3, 0.3),
#' de.prob = 0.5,
#' de.center = 2),
#' return_format = "SingleCellExperiment",
#' verbose = TRUE,
#' seed = 111)
#' ## plot
#' result <- scater::logNormCounts(simulate_result[["simulate_result"]])
#' result <- scater::runPCA(result)
#' plotPCA(result, colour_by = "group")
#'
#'
#' # (3) Simulate continous cell trajectory
#' simulate_result <- simmethods::ESCO_simulation(parameters = estimate_result[["estimate_result"]],
#' other_prior = list(batchCells = 1000,
#' nGenes = 1000,
#' type = "traj",
#' group.prob = c(0.4, 0.3, 0.3),
#' de.prob = 0.5,
#' de.center = 2),
#' return_format = "SingleCellExperiment",
#' verbose = TRUE,
#' seed = 111)
#' ## plot
#' result <- scater::logNormCounts(simulate_result[["simulate_result"]])
#' result <- scater::runPCA(result)
#' plotPCA(result, colour_by = "group")
#' }
#'
ESCO_simulation <- function(parameters,
return_format,
other_prior = NULL,
verbose = FALSE,
seed
){
##############################################################################
#### Environment ###
##############################################################################
if(!requireNamespace("ESCO", quietly = TRUE)){
message("ESCO is not installed on your device...")
message("Installing ESCO...")
devtools::install_github("JINJINT/ESCO")
}
##############################################################################
#### Check ###
##############################################################################
if(length(parameters) == 3){
tree <- parameters[["tree"]]
group <- parameters[["group"]]
parameters <- parameters[["estimate_result"]]
type <- other_prior[["type"]]
if(is.null(type)){
stop("Please input the type ('tree' or 'traj') when you want to simulate dataset with tree format information.")
}
}else{
group <- NULL
tree <- NULL
type <- NULL
}
assertthat::assert_that(class(parameters) == "escoParams")
if(!is.null(other_prior)){
parameters <- simutils::set_parameters(parameters = parameters,
other_prior = other_prior,
method = "ESCO")
}
# nCells
if(!is.null(other_prior[["nCells"]])){
parameters <- splatter::setParam(parameters, name = "nCells", value = other_prior[["nCells"]])
}
# nGenes
if(!is.null(other_prior[["nGenes"]])){
parameters <- splatter::setParam(parameters, name = "nGenes", value = other_prior[["nGenes"]])
}
# nGroup
if(!is.null(group)){
parameters <- splatter::setParam(parameters, name = "nGroups", value = length(unique(group)))
}
if(!is.null(other_prior[["prob.group"]])){
other_prior[["prob.group"]] <- round(other_prior[["prob.group"]], 2)
other_prior[["prob.group"]][length(other_prior[["prob.group"]])] <- 1 - sum(other_prior[["prob.group"]][1:(length(other_prior[["prob.group"]])-1)])
parameters <- splatter::setParam(parameters,
name = "group.prob",
value = other_prior[["prob.group"]])
}else{
if(!is.null(type)){
nGroups <- splatter::getParam(parameters, name = "nGroups")
prob.group <- c(rep(1/nGroups, nGroups-1),
1 - c(1/nGroups * c(nGroups-1)))
parameters <- splatter::setParam(parameters,
name = "group.prob",
value = prob.group)
}
}
# Get params to check
params_check <- splatter::getParams(parameters, c("nCells",
"nGenes",
"nGroups",
"group.prob",
"de.prob"))
# Return to users
message(paste0("nCells: ", params_check[['nCells']]))
message(paste0("nGenes: ", params_check[['nGenes']]))
message(paste0("nGroups: ", params_check[['nGroups']]))
message(paste0("de.group: ", params_check[['de.prob']]))
##############################################################################
#### Simulation ###
##############################################################################
if(verbose){
message("Simulating datasets using ESCO")
}
# Seed
parameters <- splatter::setParam(parameters, name = "seed", value = seed)
parameters <- splatter::setParam(parameters,
name = "deall.prob",
value = params_check[['de.prob']])
# Simulation
if(!is.null(type)){
if(type == "tree"){
message("Simulating trajectory of trees datasets by ESCO")
submethod <- "tree"
}
if(type == "traj"){
message("Simulating trajectory datasets by ESCO")
submethod <- "traj"
}
parameters <- splatter::setParam(parameters, name = "tree", value = tree)
}else{
if(params_check[["nGroups"]] == 1){
submethod <- "single"
}else if(params_check[["nGroups"]] != 1){
submethod <- "group"
}
}
simulate_detection <- peakRAM::peakRAM(
simulate_result <- ESCO::escoSimulate(parameters,
type = submethod,
verbose = verbose))
##############################################################################
#### Format Conversion ###
##############################################################################
# counts
counts <- as.matrix(SingleCellExperiment::counts(simulate_result))
# col_data
col_data <- as.data.frame(SummarizedExperiment::colData(simulate_result))
if(params_check[['nGroups']] == 1){
col_data <- data.frame("cell_name" = colnames(counts),
"group" = rep("Group1", ncol(counts)))
}else{
if(!is.null(type)){
if(type == "traj"){
col_data <- data.frame("cell_name" = colnames(counts),
"group" = paste0("Group", col_data$Path))
}else{
col_data <- data.frame("cell_name" = colnames(counts),
"group" = col_data$Group)
}
}else{
col_data <- data.frame("cell_name" = colnames(counts),
"group" = col_data$Group)
}
}
rownames(col_data) <- col_data$cell_name
# row_data
row_data <- as.data.frame(SummarizedExperiment::rowData(simulate_result))
if(params_check[['nGroups']] == 1 | !is.null(type)){
row_data <- data.frame("gene_name" = rownames(counts))
rownames(row_data) <- row_data$gene_name
}else{
group_fac <- row_data[, grep(colnames(row_data), pattern = "^DEFac")]
total_sum <- rowSums(group_fac)
de_gene <- ifelse(total_sum == params_check[['nGroups']], "no", "yes")
row_data[, 2] <- de_gene
row_data <- row_data[, -c(3:5, ncol(row_data))]
colnames(row_data) <- c("gene_name", "de_gene", colnames(group_fac))
}
# Establish SingleCellExperiment
simulate_result <- SingleCellExperiment::SingleCellExperiment(list(counts = counts),
colData = col_data,
rowData = row_data)
simulate_result <- simutils::data_conversion(SCE_object = simulate_result,
return_format = return_format)
##############################################################################
#### Ouput ###
##############################################################################
simulate_output <- list(simulate_result = simulate_result,
simulate_detection = simulate_detection)
return(simulate_output)
}
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