Revert "BASiCS_Filter moved to an individual file"

This reverts commit 057a726.
catavallejos · Jul 29, 2017 · 464bf25 · 464bf25
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diff --git a/R/BASiCS_Filter.R b/R/BASiCS_Filter.R
diff --git a/R/FunctionsPart1.R b/R/FunctionsPart1.R
@@ -1,3 +1,101 @@
+#' @title Filter for input datasets
+#'
+#' @description \code{BASiCS_Filter} indicates which transcripts and cells pass a pre-defined inclusion criteria.
+#'
+#' @param Counts Matrix of dimensions \code{q} times \code{n} whose elements corresponds to the simulated expression counts.
+#' First \code{q.bio} rows correspond to biological genes. Last \code{q-q.bio} rows correspond to technical spike-in genes.
+#' @param Tech Logical vector of length \code{q}. If \code{Tech = F} the gene is biological; otherwise the gene is spike-in.
+#' @param SpikeInput Vector of length \code{q-q.bio} whose elements indicate the simulated input concentrations for the spike-in genes.
+#' @param BatchInfo Vector of length \code{n} whose elements indicate batch information.
+#' @param MinTotalCountsPerCell Minimum value of total expression counts required per cell (biological and technical)
+#' @param MinTotalCountsPerGene Minimum value of total expression counts required per transcript (biological and technical)
+#' @param MinCellsWithExpression Minimum number of cells where expression must be detected (positive count). Criteria applied to each transcript.
+#' @param MinAvCountsPerCellsWithExpression Minimum average number of counts per cells where expression is detected. Criteria applied to each transcript.
+#'
+#' @return A list of 2 elements
+#' \describe{
+#' \item{\code{Counts}}{Filtered matrix of expression counts}
+#' \item{\code{Tech}}{Filtered vector of spike-in indicators}
+#' \item{\code{SpikeInput}}{Filtered vector of spike-in genes input molecules}
+#' \item{\code{BatchInfo}}{Filtered vector of the 'BatchInfo' argument}
+#' \item{\code{IncludeGenes}}{Inclusion indicators for transcripts}
+#' \item{\code{IncludeCells}}{Inclusion indicators for cells}
+#' }
+#'
+#' @examples
+#'
+#' set.seed(1)
+#' Counts = Counts = matrix(rpois(50*10, 2), ncol = 10)
+#' rownames(Counts) <- c(paste0("Gene", 1:40), paste0("Spike", 1:10))
+#' Tech = c(rep(FALSE,40),rep(TRUE,10))
+#' set.seed(2)
+#' SpikeInput = rgamma(10,1,1)
+#' SpikeInfo <- data.frame("SpikeID" = paste0("Spike", 1:10), "SpikeInput" = SpikeInput)
+#'
+#' Filter = BASiCS_Filter(Counts, Tech, SpikeInput,
+#'                        MinTotalCountsPerCell = 2, MinTotalCountsPerGene = 2,
+#'                        MinCellsWithExpression = 2, MinAvCountsPerCellsWithExpression = 2)
+#' SpikeInfoFilter = SpikeInfo[SpikeInfo$SpikeID %in%
+#'          names(Filter$IncludeGenes)[Filter$IncludeGenes == TRUE],]
+#' FilterData = newBASiCS_Data(Filter$Counts, Filter$Tech, SpikeInfoFilter)
+#'
+#' @seealso \code{\link[BASiCS]{BASiCS_Data-class}}
+#'
+#' @author Catalina A. Vallejos \email{cnvallej@@uc.cl}
+#'
+#' @references Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data.
+BASiCS_Filter <- function(Counts, Tech, SpikeInput, BatchInfo = NULL,
+                          MinTotalCountsPerCell = 2, MinTotalCountsPerGene = 2,
+                          MinCellsWithExpression = 2, MinAvCountsPerCellsWithExpression = 2)
+{
+  q = length(Tech)
+  q.bio = q - sum(SpikeInput)
+  n = ncol(Counts)
+
+  CellIndex = 1:n
+  GeneIndex = 1:q
+
+  # Remove cells with zero counts in either biological or technical genes
+  IncludeCells = ifelse(apply(Counts[!Tech,],2,sum)>0 & apply(Counts[Tech,],2,sum)>0, T, F)
+  if(sum(IncludeCells) == 0) stop('All cells have zero biological or technical counts (across all transcripts) \n')
+  IncludeCells = ifelse(apply(Counts,2,sum) >= MinTotalCountsPerCell, IncludeCells, F)
+  Counts1 = Counts[,IncludeCells]
+
+  # Remove transcripts with zero counts across all cells
+  IncludeBio = ifelse(apply(Counts1[!Tech,],1,sum) >= MinTotalCountsPerGene, T, F)
+  IncludeTech = ifelse(apply(Counts1[Tech,],1,sum) >= MinTotalCountsPerGene, T, F)
+
+  # Remove transcripts expressed in less than 'MinExpressedCells' cells
+  NonZero <- I(Counts1>0)
+  IncludeBio = ifelse(apply(NonZero[!Tech,], 1, sum) >= MinCellsWithExpression, IncludeBio, F)
+  IncludeTech = ifelse(apply(NonZero[Tech,], 1, sum) >= MinCellsWithExpression, IncludeTech, F)
+
+  # Remove transcripts with low counts in the cells where they are expressed
+  if(min(apply(NonZero[!Tech,], 1, sum)) == 0 & MinCellsWithExpression == 0) warning("Some genes have zero counts in all cells. These should be removed before running the analysis (use 'MinCellsWithExpression' > 0).")
+  IncludeBio = ifelse(apply(Counts1[!Tech,], 1, sum) >= MinAvCountsPerCellsWithExpression * apply(NonZero[!Tech,], 1, sum), IncludeBio, F)
+  IncludeTech = ifelse(apply(Counts1[Tech,], 1, sum) >= MinAvCountsPerCellsWithExpression * apply(NonZero[Tech,], 1, sum), IncludeTech, F)
+
+  if(!is.null(BatchInfo))
+  {
+    list("Counts" = Counts1[c(IncludeBio,IncludeTech),], 
+         "Tech" = Tech[c(IncludeBio,IncludeTech)],
+         "SpikeInput" = SpikeInput[IncludeTech], 
+         "BatchInfo" = BatchInfo[IncludeCells],
+         "IncludeGenes" = c(IncludeBio,IncludeTech), 
+         "IncludeCells" = IncludeCells)
+  }
+  else
+  {
+    list("Counts" = Counts1[c(IncludeBio,IncludeTech),], 
+         "Tech" = Tech[c(IncludeBio,IncludeTech)],
+         "SpikeInput" = SpikeInput[IncludeTech], 
+         "BatchInfo" = NULL,
+         "IncludeGenes" = c(IncludeBio,IncludeTech), 
+         "IncludeCells" = IncludeCells)    
+  }
+
+}
+
 #' @title Creates a BASiCS_Data object from a matrix of expression counts and experimental information about spike-in genes
 #'
 #' @description \code{newBASiCS_Data} creates a \code{\link[BASiCS]{BASiCS_Data-class}} object from