can set binarizeMethod to a hard cutoff

R/bkend.R

@@ -11,6 +11,8 @@ binarize = function(x, method = "median") {
 		pi = median(x@x)
 	} else if (method == "naive") {
 		pi = min(x@x)
+	} else if ((is.numeric(method)) & (method >= 0)) {
+		pi = method
 	} else {
 		stop("Unrecognized binarization method! genesorteR stopped.")
 	}

R/sortGenes.R

@@ -10,8 +10,9 @@
 #' This is equal to the median of all non-zero entries in the matrix and is
 #' returned in \code{cutoff}. When binarizeMethod is "naive", all non-zero
 #' entries are kept and the minimum value of non-zero entries is returned in
-#' \code{cutoff}. When the input matrix \code{x} has already been binarized, set
-#' \code{binarizeMethod} to "naive".
+#' \code{cutoff}. When the input matrix \code{x} has already been binarized, 
+#' set \code{binarizeMethod} to "naive". You can set a specific cutoff value 
+#' for binarization, by setting #' \code{binarizeMethod} to a numeric value >= 0.
 #'
 #' The specificity score matrix \code{specScore} is calculated as
 #' \code{condGeneProb * postClustProb}. Therefore, it simply balances the
@@ -30,7 +31,8 @@
 #' @param classLabels A numeric or character vector or a factor of the same
 #'   length as ncol(x) that represents cell cluster assignments. It will be
 #'   coerced to a factor whose levels are the cell cluster names.
-#' @param binarizeMethod Either "median" (default) or "naive". See Details.
+#' @param binarizeMethod Either "median" (default) or "naive" or a numeric cutoff. 
+#' See Details.
 #' @param returnInput Return the input matrix and cell classes? \code{TRUE} by 
 #' default.
 #' @param cores A number greater than zero (1 by default) that indicates how