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#' @name BASiCS_VarThresholdSearchHVG
#' @aliases BASiCS_VarThresholdSearchHVG BASiCS_VarThresholdSearchHVG_LVG
#'
#' @title Detection method for highly and lowly variable genes using
#' a grid of variance contribution thresholds
#'
#' @description Detection method for highly and lowly variable genes
#' using a grid of variance contribution thresholds. Only used when
#' HVG/LVG are found based on the variance decomposition.
#'
#' @param Chain an object of class \code{\linkS4class{BASiCS_Chain}}
#' @param Task See \code{?BASiCS_DetectVG}.
#' @param VarThresholdsGrid Grid of values for the variance contribution
#' threshold (they must be contained in (0,1))
#' @param EFDR Target for expected false discovery rate related to
#' HVG/LVG detection. Default: \code{EFDR = 0.10}.
#' @param Progress If \code{Progress = TRUE}, partial output is
#' printed in the console. Default: \code{Progress = TRUE}.
#' @param ... Passed to methods.
#' @examples
#'
#' data(ChainSC)
#'
#' BASiCS_VarThresholdSearchHVG(ChainSC,
#' VarThresholdsGrid = seq(0.55,0.65,by=0.01),
#' EFDR = 0.10)
#' BASiCS_VarThresholdSearchLVG(ChainSC,
#' VarThresholdsGrid = seq(0.35,0.45,by=0.01),
#' EFDR = 0.10)
#'
#' @details See vignette
#'
#' @return
#' \describe{
#' \item{\code{BASiCS_VarThresholdSearchHVG}}{A table displaying the results of
#' highly variable genes detection for different variance
#' contribution thresholds.}
#' \item{\code{BASiCS_VarThresholdSearchLVG}}{A table displaying the results of
#' lowly variable genes detection for different variance
#' contribution thresholds.}
#' }
#'
#'
#' @seealso \code{\linkS4class{BASiCS_Chain}}
#'
#' @author Catalina A. Vallejos \email{cnvallej@@uc.cl}
#'
#' @references
#' Vallejos, Marioni and Richardson (2015). PLoS Computational Biology.
#'
#' @rdname BASiCS_VarThresholdSearchHVG_LVG
#' @export
BASiCS_VarThresholdSearchVG <- function(Chain,
Task = c("HVG", "LVG"),
VarThresholdsGrid,
EFDR = 0.1,
Progress = TRUE) {
if (!is(Chain, "BASiCS_Chain")) {
stop("'object' is not a BASiCS_Chain class object.")
}
if (sum(VarThresholdsGrid < 0) > 0 |
sum(VarThresholdsGrid > 1) > 0 |
sum(!is.finite(VarThresholdsGrid)) > 0) {
stop("Variance contribution thresholds must be contained in (0,1).")
}
Table <- as.data.frame(matrix(0, nrow = length(VarThresholdsGrid), ncol = 5))
colnames(Table) <- c("Var. Threshold (%)", "EFDR (%)", "EFNR (%)",
"Optimal evidence thres.", "# Detected genes")
if(!is.null(Chain@parameters$epsilon)) {
stop("Function not available for residual overdispersion parameters")
} else {
for (i in seq_along(VarThresholdsGrid)) {
VarThreshold <- VarThresholdsGrid[i]
if (Progress) {
message(
"Evaluating variance contribution threshold = ",
100 * VarThreshold, " % ... \n")
}
suppressMessages(
DetectHVG <- BASiCS_DetectVG(
Chain,
Task = Task,
EFDR = EFDR,
VarThreshold = VarThreshold
)
)
Table[i, ] <- c(
100 * VarThreshold,
round(100 * DetectHVG@EFDR, 2),
round(100 * DetectHVG@EFNR, 2),
DetectHVG@ProbThreshold,
sum(.VG(DetectHVG))
)
}
}
return(Table)
}
#' @name BASiCS_VarThresholdSearchHVG
#' @aliases BASiCS_VarThresholdSearchLVG BASiCS_VarThresholdSearchHVG_LVG
#' @rdname BASiCS_VarThresholdSearchHVG_LVG
#' @export
BASiCS_VarThresholdSearchHVG <- function(...) {
# .Deprecated("BASiCS_VarThresholdSearchVG")
BASiCS_VarThresholdSearchVG(..., Task = "HVG")
}
#' @name BASiCS_VarThresholdSearchLVG
#' @aliases BASiCS_VarThresholdSearchLVG BASiCS_VarThresholdSearchHVG_LVG
#' @rdname BASiCS_VarThresholdSearchHVG_LVG
#' @export
BASiCS_VarThresholdSearchLVG <- function(...) {
# .Deprecated("BASiCS_VarThresholdSearchVG")
BASiCS_VarThresholdSearchVG(..., Task = "LVG")
}
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