R/scRecoverTest.R

In miaozhun/scRecover: scRecover for imputation of single-cell RNA-seq data

#' scRecoverTest: A test dataset for scRecover
#'
#' A test dataset containing a single-cell RNA-seq (scRNA-seq) read counts matrix and its cell type information.
#'
#' \itemize{
#'   \item counts. A matrix of raw read counts of scRNA-seq data which has 200 genes (rows) and 150 cells (columns).
#'   \item labels. A vector of integer specifying the two cell types in \code{counts}. Also could be generated by: \code{labels <- c(rep(1,50), rep(2,100))}.
#'   \item oneCell. A vector of a cell's raw read counts for 24538 gene.
#' }
#'
#' @name scRecoverTest
#' @aliases counts labels oneCell
#' @docType data
#' @keywords data
#' @usage data(scRecoverTest)
#' @format
#' \itemize{
#'   \item counts. A non-negative integer matrix of scRNA-seq raw read counts, rows are genes and columns are cells.
#'   \item labels. A vector of integer specifying the cell types in the read counts matrix, corresponding to the columns of \code{counts}.
#'   \item oneCell. A non-negative vector of scRNA-seq raw read counts of a cell for each gene.
#' }
#' @source Petropoulos S, et al. Cell, 2016, 165(4): 1012-1026.
#' @seealso
#' \code{\link{scRecover}}, for imputation of single-cell RNA-seq data.
#'
#' \code{\link{estDropoutNum}}, for estimating dropout gene number in a cell.
#'
#' \code{\link{countsSampling}}, for downsampling the read counts in a cell.
#'
#' \code{\link{normalization}}, for normalization of single-cell RNA-seq data.
#'
#' @examples
#' # Load test data for scRecover
#' data(scRecoverTest)
#'
#'
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