Add Splat parameters vignette

Fix spelling

.Rbuildignore

@@ -9,3 +9,5 @@
 ^index\.md$
 ^_pkgdown\.yml$
 ^pkgdown$
+^doc$
+^Meta$

.gitignore

@@ -26,3 +26,5 @@ vignettes/*.pdf
 *.knit.md
 .Rproj.user
 inst/doc
+doc
+Meta

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: splatter
 Type: Package
 Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 1.7.4
-Date: 2019-04-17
+Version: 1.7.5
+Date: 2019-04-18
 Author: Luke Zappia
 Authors@R:
     c(person("Luke", "Zappia", role = c("aut", "cre"),
@@ -61,11 +61,13 @@ Suggests:
     BASiCS,
     zinbwave,
     SparseDC,
-    BiocManager
+    BiocManager,
+    spelling
 biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing,
     Software, ImmunoOncology
 URL: https://github.com/Oshlack/splatter
 BugReports: https://github.com/Oshlack/splatter/issues
 RoxygenNote: 6.1.1
 Encoding: UTF-8
 VignetteBuilder: knitr
+Language: en-GB

NEWS.md

@@ -1,3 +1,8 @@
+## Version 1.7.5 (2018-04-18)
+
+* Add Splat parameters vignette
+* Fix spelling
+
 ## Version 1.7.4 (2018-04-17)
 
 * Allow SplatParams parameters to be set in any order
@@ -53,7 +58,7 @@
 
 * Fix bug in getLNormFactors when reversing factors less than one
 * Update documentation to new Roxygen version (6.1.0)
-* Change varible name in vignette for compatibility with scater
+* Change variable name in vignette for compatibility with scater
 * Add suggested package checks to tests
 
 ## Version 1.5.1 (2018-06-12)
@@ -226,7 +231,7 @@
 
 ## Version 0.99.13 (2017-03-25)
 
-* Modify how Lun2Params stores gene paramters to use data.frame
+* Modify how Lun2Params stores gene parameters to use data.frame
 * Move sampling of genes/cells to lun2Simulate
 * Return to old Lun2 nGenes estimate
 
@@ -236,7 +241,7 @@
 * Update compareSCESets plots
 * Modify Lun2 nGenes estimate
 * Modify how addFeatureStats names columns
-* Add infinte bcv.df warning to splatSimulate
+* Add infinite bcv.df warning to splatSimulate
 
 ## Version 0.99.11 (2017-03-20)
 
@@ -338,7 +343,7 @@
 
 * Redesign how parameters are stored
 * Each simulation now has it's own S4 Params class
-* Modify exisiting simulations to use new parameter objects
+* Modify existing simulations to use new parameter objects
 
 ## Version 0.6.0 (2016-10-12)
 

R/AllClasses.R

@@ -332,18 +332,18 @@ setClass("LunParams",
 #'     \item{\emph{Gene parameters}}{
 #'         \describe{
 #'             \item{\code{gene.params}}{A \code{data.frame} containing gene
-#'             parameters with two coloumns: \code{Mean} (mean expression for
+#'             parameters with two columns: \code{Mean} (mean expression for
 #'             each gene) and \code{Disp} (dispersion for each gene).}
 #'             \item{\code{zi.params}}{A \code{data.frame} containing
-#'             zero-inflated gene parameters with three coloumns: \code{Mean}
+#'             zero-inflated gene parameters with three columns: \code{Mean}
 #'             (mean expression for each gene), \code{Disp} (dispersion for
 #'             each, gene), and \code{Prop} (zero proportion for each gene).}
 #'         }
 #'     }
 #'     \item{\code{[nPlates]}}{The number of plates to simulate.}
 #'     \item{\emph{Plate parameters}}{
 #'         \describe{
-#'             \item{\code{plate.ingroup}}{Character vecotor giving the plates
+#'             \item{\code{plate.ingroup}}{Character vector giving the plates
 #'             considered to be part of the "ingroup".}
 #'             \item{\code{plate.mod}}{Plate effect modifier factor. The plate
 #'             effect variance is divided by this value.}
@@ -492,7 +492,7 @@ setClass("SCDDParams",
 #'     \item{\emph{Gene parameters}}{
 #'         \describe{
 #'             \item{\code{gene.params}}{A \code{data.frame} containing gene
-#'             parameters with two coloumns: \code{Mean} (mean expression for
+#'             parameters with two columns: \code{Mean} (mean expression for
 #'             each biological gene) and \code{Delta} (cell-to-cell
 #'             heterogeneity for each biological gene).}
 #'         }
@@ -506,7 +506,7 @@ setClass("SCDDParams",
 #'     \item{\emph{Cell parameters}}{
 #'         \describe{
 #'             \item{\code{cell.params}}{A \code{data.frame} containing gene
-#'             parameters with two coloumns: \code{Phi} (mRNA content factor for
+#'             parameters with two columns: \code{Phi} (mRNA content factor for
 #'             each cell, scaled to sum to the number of cells in each batch)
 #'             and \code{S} (capture efficient for each cell).}
 #'         }

R/AllGenerics.R

@@ -36,7 +36,7 @@ setGeneric("getParam", function(object, name) {standardGeneric("getParam")})
 #'
 #' @param object object to set parameter in.
 #' @param name name of the parameter to set.
-#' @param value value to set the paramter to.
+#' @param value value to set the parameter to.
 #'
 #' @return Object with new parameter value.
 #'
@@ -57,7 +57,7 @@ setGeneric("setParam", function(object, name, value) {
 #'
 #' @param object object to set parameter in.
 #' @param name name of the parameter to set.
-#' @param value value to set the paramter to.
+#' @param value value to set the parameter to.
 #'
 #' @return Object with new parameter value.
 #'

R/BASiCS-simulate.R

@@ -18,7 +18,7 @@
 #'
 #' @references
 #' Vallejos CA, Marioni JC, Richardson S. BASiCS: Bayesian Analysis of
-#' Single-Cell Sequencing data. PLoS Comput. Biol. (2015).
+#' Single-Cell Sequencing data. PLoS Computational Biology (2015).
 #'
 #' Paper: \url{10.1371/journal.pcbi.1004333}
 #'

R/compare.R

@@ -24,7 +24,7 @@
 #'             \item{\code{Variances}}{Boxplot of variance distribution.}
 #'             \item{\code{MeanVar}}{Scatter plot with fitted lines showing the
 #'             mean-variance relationship.}
-#'             \item{\code{LibraySizes}}{Boxplot of the library size
+#'             \item{\code{LibrarySizes}}{Boxplot of the library size
 #'             distribution.}
 #'             \item{\code{ZerosGene}}{Boxplot of the percentage of each gene
 #'             that is zero.}
@@ -227,7 +227,7 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
 #'             \item{\code{Variances}}{Boxplot of variance differences.}
 #'             \item{\code{MeanVar}}{Scatter plot showing the difference from
 #'             the reference variance across expression ranks.}
-#'             \item{\code{LibraySizes}}{Boxplot of the library size
+#'             \item{\code{LibraeySizes}}{Boxplot of the library size
 #'             differences.}
 #'             \item{\code{ZerosGene}}{Boxplot of the differences in the
 #'             percentage of each gene that is zero.}
@@ -411,7 +411,7 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
         geom_hline(yintercept = 0, colour = "red") +
         geom_boxplot() +
         scale_colour_manual(values = colours) +
-        ylab(paste("Rank difference libray size")) +
+        ylab(paste("Rank difference library size")) +
         ggtitle("Difference in library sizes") +
         theme_minimal()
 

R/lun2-simulate.R

@@ -19,9 +19,9 @@
 #' Library size factors are also applied and optionally a zero-inflated
 #' negative-binomial can be used.
 #'
-#' If the number of genes to simulate differs from the number of provied gene
+#' If the number of genes to simulate differs from the number of provided gene
 #' parameters or the number of cells to simulate differs from the number of
-#' library sizes the relevant paramters will be sampled with a warning. This
+#' library sizes the relevant parameters will be sampled with a warning. This
 #' allows any number of genes or cells to be simulated regardless of the
 #' number in the dataset used in the estimation step but has the downside that
 #' some genes or cells may be simulated multiple times.
@@ -145,7 +145,7 @@ lun2Simulate <- function(params = newLun2Params(), zinb = FALSE,
         }
     }
 
-    if (verbose) {message("Simulating libray size factors...")}
+    if (verbose) {message("Simulating library size factors...")}
     lib.facs <- lib.sizes / mean(lib.sizes)
     lib.facs <- sample(lib.facs, nCells, replace = TRUE) * lib.mod
     cells$LibSizeFac <- lib.facs

R/sparseDC-estimate.R

@@ -7,7 +7,7 @@
 #'        containing count data to estimate parameters from.
 #' @param conditions numeric vector giving the condition each cell belongs to.
 #' @param nclusters number of cluster present in the dataset.
-#' @param norm logical, whether to libray size normalise counts before
+#' @param norm logical, whether to library size normalise counts before
 #'        estimation. Set this to FALSE if counts is already normalised.
 #' @param params PhenoParams object to store estimated values in.
 #'

R/splat-simulate.R

@@ -396,7 +396,7 @@ splatSimBatchCellMeans <- function(sim, params) {
 #'
 #' Simulate differential expression. Differential expression factors for each
 #' group are produced using \code{\link{getLNormFactors}} and these are added
-#' along with updated means for each group. For paths care is taked to make sure
+#' along with updated means for each group. For paths care is taken to make sure
 #' they are simulated in the correct order.
 #'
 #' @param sim SingleCellExperiment to add differential expression to.
@@ -696,7 +696,7 @@ splatSimTrueCounts <- function(sim, params) {
 
 #' Simulate dropout
 #'
-#' A logistic function is used to form a relationshop between the expression
+#' A logistic function is used to form a relationship between the expression
 #' level of a gene and the probability of dropout, giving a probability for each
 #' gene in each cell. These probabilities are used in a Bernoulli distribution
 #' to decide which counts should be dropped.
@@ -773,7 +773,7 @@ splatSimDropout <- function(sim, params) {
 
     if (dropout.type != "none") {
 
-        # Generate probabilites based on expression
+        # Generate probabilities based on expression
         drop.prob <- sapply(seq_len(nCells), function(idx) {
             eta <- log(cell.means[, idx])
             return(logistic(eta, x0 = dropout.mid[idx], k = dropout.shape[idx]))
@@ -834,7 +834,7 @@ getLNormFactors <- function(n.facs, sel.prob, neg.prob, fac.loc, fac.scale) {
 #' Identify the correct order to process paths so that preceding paths have
 #' already been simulated.
 #'
-#' @param path.from vector giving the path endpoints that each path orginates
+#' @param path.from vector giving the path endpoints that each path originates
 #'        from.
 #'
 #' @return Vector giving the order to process paths in.

R/utils.R

@@ -6,7 +6,7 @@
 #' @param x0 midpoint parameter. Gives the centre of the function.
 #' @param k shape parameter. Gives the slope of the function.
 #'
-#' @return Value of logistic funciton with given parameters
+#' @return Value of logistic function with given parameters
 logistic <- function(x, x0, k) {
     1 / (1 + exp(-k * (x - x0)))
 }

R/zinb-estimate.R

@@ -9,7 +9,7 @@
 #' @param design.genes design matrix of gene-level covariates.
 #' @param common.disp logical. Whether or not a single dispersion for all
 #'        features is estimated.
-#' @param iter.init number of iterations to use for initalization.
+#' @param iter.init number of iterations to use for initialization.
 #' @param iter.opt number of iterations to use for optimization.
 #' @param stop.opt stopping criterion for optimization.
 #' @param params ZINBParams object to store estimated values in.

README.md

@@ -46,7 +46,7 @@ aren't required for core functionality).
 ## Getting started
 
 Once installed the best place to get started is the vignette. For most users
-the most convient way to access this is online [here][vignette].
+the most convenient way to access this is online [here][vignette].
 
 Alternatively, if you chose to build the vignette, you can load Splatter, then
 browse the vignettes: