R/TestData.R

In sam-uofl/SwarnSeq: Detection of Differentially Expressed Genes from Single Cell RNA-seq Data

#' TestData: A test scRNA-seq real dataset for SwarnSeq
#'
#' A toy dataset containing a single-cell RNA-seq (scRNA-seq) read counts matrix, ERCC spike-ins counts and spike-in concentration.
#'
#'
#' \itemize{
#'   \item CountData. A matrix of raw read counts of scRNA-seq data which has 200 genes (rows) and 200 cells (columns).
#'   \item SpikeCounts. A matrix of read counts for spik-in transcripts to estimate the capture efficiencies of the cells.
#'   \item SpikeConc. A vector of the molecular concentration for spike-in transcripts.
#'   \item group. A vector specifying the two groups and can be generated by: \code{group <- c(rep(1,100), rep(2,100))}
#'   \item CellCluster. A vector specifying the cell cluster memberships of the cells in \code{CountData} and for example can be generated by: \code{CellCluster <- c(rep(1,20), rep(2,30), rep(3, 40), rep(4, 50), rep(5, 60))}.
#' }
#'
#' @name TestData
#'
#' @aliases CountData SpikeCounts SpikConc
#'
#' @docType data
#'
#' @keywords data
#'
#' @usage data(TestData)
#'
#' @format
#'
#' \itemize{
#'   \item CountData. A matrix of raw read counts of scRNA-seq data which has 500 genes (rows) and 200 cells (columns).
#'   \item SpikeCounts. A matrix of read counts for spik-in transcripts to estimate the capture efficiencies of the cells.
#'   \item SpikeConc. A vector of the molecular concentration for spike-in transcripts.
#'   \item group. A vector specifying the two groups and can be generated by: \code{group <- c(rep(1,100), rep(2,100))}.
#'   \item CellCluster. A vector specifying the cell cluster memberships of the cells in \code{CountData} and for example can be generated by: \code{CellCluster <- c(rep(1,20), rep(2,30), rep(3, 40), rep(4, 50), rep(5, 60))}.
#' }
#'
#' @source Petropoulos S, et al. Cell, 2016, 165(4): 1012-1026.
#'
#' @examples
#'
#' # Load test data for DEsingle
#'
#' data(TestData)
#' CountData = TestData$CountData
#' SpikeCounts = TestData$SpikeCounts
#' SpikConc = TestData$SpikeConc
#'
#' # Specifying the two groups to be compared
#' # The sample number in group 1 and group 2 is 100 and 100 cells respectively
#' group <- c(rep(1,100), rep(2,100))
#'
#' # Specifying the cluster-memberships of the cells.
#' CellCluster <- c(rep(1,20), rep(2,30), rep(3, 40), rep(4, 50), rep(5, 60))
#'

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