Run checks

DESCRIPTION

 Package: splatter
 Type: Package
 Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 0.2.0
+Version: 0.2.3
 Date: 2016-10-06
 Author: Luke Zappia
 Authors@R: as.person(c(
@@ -20,7 +20,9 @@ Depends:
     scater
 Imports: limma,
     fitdistrplus,
-    edgeR
+    edgeR,
+    stats,
+    locfit
 Suggests:
     testthat
 biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing,

NAMESPACE

 # Generated by roxygen2: do not edit by hand
 
+S3method(estimateParams,SCESet)
+S3method(estimateParams,matrix)
 S3method(print,splatParams)
 export(checkParams)
 export(defaultParams)
@@ -8,3 +10,6 @@ export(getParams)
 export(mergeParams)
 export(setParams)
 export(splatParams)
+importFrom(stats,dnbinom)
+importFrom(stats,median)
+importFrom(stats,nls)

R/estimate.R

@@ -27,28 +27,31 @@
 estimateParams <- function(x, params = NULL) UseMethod("estimateParams")
 
 #' @rdname estimateParams
-estimateParams.SCESet <- function(sce, params = NULL) {
-    counts <- scater::counts(sce)
+#' @export
+estimateParams.SCESet <- function(x, params = NULL) {
+    counts <- scater::counts(x)
     estimateParams(counts, params)
 }
 
 #' @rdname estimateParams
-estimateParams.matrix <- function(counts, params = NULL) {
+#' @importFrom stats median
+#' @export
+estimateParams.matrix <- function(x, params = NULL) {
 
     if (is.null(params)) {
         params <- splatParams()
     }
 
     # Normalise for library size and remove all zeros
-    lib.sizes <- colSums(counts)
+    lib.sizes <- colSums(x)
     lib.med <- median(lib.sizes)
-    norm.counts <- t(t(counts) / lib.sizes * lib.med)
+    norm.counts <- t(t(x) / lib.sizes * lib.med)
     norm.counts <- norm.counts[rowSums(norm.counts > 0) > 1, ]
 
     params <- estMeanParams(norm.counts, params)
-    params <- estLibParams(counts, params)
+    params <- estLibParams(x, params)
     params <- estOutlierParams(norm.counts, params)
-    params <- estBCVParams(counts, params)
+    params <- estBCVParams(x, params)
     params <- estDropoutParams(norm.counts, params)
 
     return(params)
@@ -65,12 +68,14 @@ estimateParams.matrix <- function(counts, params = NULL) {
 #'
 #' @return splatParams object with estimated values.
 #' @examples
+#' \dontrun{
 #' data("sc_example_counts")
 #' norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
 #'                   median(colSums(sc_example_counts)))
-#' params <- params()
+#' params <- splatParams()
 #' params <- estMeanParams(norm_ex_counts, params)
 #' params
+#' }
 estMeanParams <- function(norm.counts, params) {
 
     means <- rowMeans(norm.counts)
@@ -95,10 +100,12 @@ estMeanParams <- function(norm.counts, params) {
 #'
 #' @return splatParams object with estimated values.
 #' @examples
+#' \dontrun{
 #' data("sc_example_counts")
-#' params <- params()
+#' params <- splatParams()
 #' params <- estLibParams(sc_example_counts, params)
 #' params
+#' }
 estLibParams <- function(counts, params) {
 
     lib.sizes <- colSums(counts)
@@ -132,12 +139,14 @@ estLibParams <- function(counts, params) {
 #'
 #' @return splatParams object with estimated values.
 #' @examples
+#' \dontrun{
 #' data("sc_example_counts")
 #' norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
 #'                   median(colSums(sc_example_counts)))
-#' params <- params()
+#' params <- splatParams()
 #' params <- estOutlierParams(norm_ex_counts, params)
 #' params
+#' }
 estOutlierParams <- function(norm.counts, params) {
 
     means <- rowMeans(norm.counts)
@@ -176,10 +185,12 @@ estOutlierParams <- function(norm.counts, params) {
 #'
 #' @return spaltParams object with estimated values.
 #' @examples
+#' \dontrun{
 #' data("sc_example_counts")
-#' params <- params()
+#' params <- splatParams()
 #' params <- estBCVParams(sc_example_counts, params)
 #' params
+#' }
 estBCVParams <- function(counts, params) {
 
     # Add dummy design matrix to avoid print statement
@@ -218,11 +229,14 @@ estBCVParams <- function(counts, params) {
 #'
 #' @return Params object with estimated values.
 #' @examples
+#' \dontrun{
 #' data("sc_example_counts")
 #' norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
 #'                   median(colSums(sc_example_counts)))
-#' params <- params()
+#' params <- splatParams()
 #' params <- estMeanParams(norm_ex_counts, params)
+#' }
+#' @importFrom stats nls dnbinom median
 estDropoutParams <- function(norm.counts, params) {
 
     means <- rowMeans(norm.counts)

R/utils.R

@@ -7,8 +7,6 @@
 #' @param k shape parameter. Gives the slope of the function.
 #'
 #' @return RETURN DESCRIPTION
-#' @examples
-#' logistic(0, 1, 1)
 logistic <- function(x, x0, k) {
     1 / (1 + exp(-k * (x - x0)))
 }
\ No newline at end of file

man/estBCVParams.Rd

@@ -20,9 +20,11 @@ Parameters are estimated using the \code{estimateDisp} function in the
 of freedom. See \code{\link{estimateDisp}} for details.
 }
 \examples{
+\dontrun{
 data("sc_example_counts")
-params <- params()
+params <- splatParams()
 params <- estBCVParams(sc_example_counts, params)
 params
 }
+}
 

man/estDropoutParams.Rd

@@ -35,10 +35,12 @@ at a plot of log2 mean expression vs the difference between observed and
 expected number of zeros across all genes.
 }
 \examples{
+\dontrun{
 data("sc_example_counts")
 norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
                   median(colSums(sc_example_counts)))
-params <- params()
+params <- splatParams()
 params <- estMeanParams(norm_ex_counts, params)
 }
+}
 

man/estLibParams.Rd

@@ -20,9 +20,11 @@ parameters are added to the params object. See \code{\link{fitdist}} for
 details on the fitting.
 }
 \examples{
+\dontrun{
 data("sc_example_counts")
-params <- params()
+params <- splatParams()
 params <- estLibParams(sc_example_counts, params)
 params
 }
+}
 

man/estMeanParams.Rd

@@ -20,11 +20,13 @@ simulate gene expression means using the 'moment matching estimation' method
 of \code{\link{fitdist}}.
 }
 \examples{
+\dontrun{
 data("sc_example_counts")
 norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
                   median(colSums(sc_example_counts)))
-params <- params()
+params <- splatParams()
 params <- estMeanParams(norm_ex_counts, params)
 params
 }
+}
 

man/estOutlierParams.Rd

@@ -31,11 +31,13 @@ factors in order to estimate the outlier factor location and scale
 parameters. See \code{\link{fitdist}} for details on the fitting.
 }
 \examples{
+\dontrun{
 data("sc_example_counts")
 norm_ex_counts <- t(t(sc_example_counts) / colSums(sc_example_counts) *
                   median(colSums(sc_example_counts)))
-params <- params()
+params <- splatParams()
 params <- estOutlierParams(norm_ex_counts, params)
 params
 }
+}
 

man/estimateParams.Rd

@@ -8,9 +8,9 @@
 \usage{
 estimateParams(x, params = NULL)
 
-\method{estimateParams}{SCESet}(sce, params = NULL)
+\method{estimateParams}{SCESet}(x, params = NULL)
 
-\method{estimateParams}{matrix}(counts, params = NULL)
+\method{estimateParams}{matrix}(x, params = NULL)
 }
 \arguments{
 \item{x}{either a counts matrix or an SCESet object containing count data to

man/logistic.Rd

@@ -19,7 +19,4 @@ RETURN DESCRIPTION
 \description{
 Implementation of the logistic function
 }
-\examples{
-logistic(0, 1, 1)
-}