Removed expensive examples

R/AllGenerics.R

@@ -32,7 +32,7 @@ setGeneric(
 #' @return A matrix of normalized counts in log-scale.
 #' 
 #' @examples
-#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
 #' obj <- sconeExperiment(mat)
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
@@ -62,7 +62,7 @@ setGeneric(
 #' @return The design matrix.
 #' 
 #' @examples
-#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
 #' obj <- sconeExperiment(mat)
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
@@ -92,7 +92,7 @@ setGeneric(
 #' @return A \code{SconeExperiment} object with selected method data.
 #' 
 #' @examples
-#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
 #' obj <- sconeExperiment(mat)
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
@@ -168,7 +168,7 @@ setGeneric(
 #'   get_score_ranks,SconeExperiment-method
 #'   
 #' @examples
-#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
 #' obj <- sconeExperiment(mat)
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
@@ -195,7 +195,7 @@ setGeneric(
 #'
 #' @aliases get_params get_params,SconeExperiment-method
 #' @examples
-#' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
+#' mat <- matrix(rpois(500, lambda = 5), ncol=10)
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
 #' obj <- sconeExperiment(mat)
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),

R/SCONE_DEFAULTS.R

@@ -150,16 +150,7 @@ TMM_FN = function(ei){
 #' @param ei Numerical matrix. (rows = genes, cols = samples).
 #' @return SCRAN size factor normalized matrix.
 #' 
-#' @examples
-#' set.seed(100)
-#' popsize <- 800
-#' ngenes <- 10000
-#' all.facs <- 2^rnorm(popsize, sd=0.5)
-#' mu1 = matrix(rep(all.facs*10,ngenes),nrow = ngenes,byrow = TRUE)
-#' mu2 = matrix(c(rep(2^rnorm(ngenes, sd=0.5),popsize/2), rep(2^rnorm(ngenes, sd=0.5),popsize/2)),ncol = popsize)
-#' ei <- matrix(rnbinom(ngenes*popsize, mu=as.vector(mu1*mu2), size=1), ncol=popsize)
-#' eo <- SCRAN_FN(ei)
-#' 
+
 SCRAN_FN = function(ei){
   clusters <- quickCluster(ei, min.size = 20)
   size_fac = computeSumFactors(ei, cluster=clusters)