Run checks

NAMESPACE

@@ -15,6 +15,8 @@ S3method(scDDEstimate,default)
 S3method(scDDEstimate,matrix)
 S3method(simpleEstimate,SingleCellExperiment)
 S3method(simpleEstimate,matrix)
+S3method(sparseDCEstimate,SingleCellExperiment)
+S3method(sparseDCEstimate,matrix)
 S3method(splatEstimate,SingleCellExperiment)
 S3method(splatEstimate,matrix)
 S3method(zinbEstimate,SingleCellExperiment)
@@ -43,6 +45,7 @@ export(newMFAParams)
 export(newPhenoParams)
 export(newSCDDParams)
 export(newSimpleParams)
+export(newSparseDCParams)
 export(newSplatParams)
 export(newZINBParams)
 export(phenoEstimate)
@@ -53,6 +56,8 @@ export(setParam)
 export(setParams)
 export(simpleEstimate)
 export(simpleSimulate)
+export(sparseDCEstimate)
+export(sparseDCSimulate)
 export(splatEstimate)
 export(splatSimulate)
 export(splatSimulateGroups)
@@ -68,6 +73,7 @@ exportClasses(MFAParams)
 exportClasses(PhenoParams)
 exportClasses(SCDDParams)
 exportClasses(SimpleParams)
+exportClasses(SparseDCParams)
 exportClasses(SplatParams)
 exportClasses(ZINBParams)
 importFrom(BiocParallel,SerialParam)

R/params-functions.R

@@ -196,7 +196,7 @@ showDFs <- function(dfs, not.default) {
     checkmate::check_list(dfs, types = "data.frame", any.missing = FALSE,
                           min.len = 1)
     checkmate::check_logical(not.default, any.missing = FALSE,
-                             len = length(values))
+                             len = length(dfs))
 
     names(dfs)[not.default] <- toupper(names(dfs)[not.default])
 

R/sparseDC-estimate.R

@@ -5,7 +5,7 @@
 #'
 #' @param counts either a counts matrix or an SingleCellExperiment object
 #'        containing count data to estimate parameters from.
-#' @param condition numeric vector giving the condition each cell belongs to.
+#' @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
 #'        estimation. Set this to FALSE if counts is already normalised.
@@ -13,12 +13,13 @@
 #'
 #' @details
 #' The \code{nGenes} and \code{nCells} parameters are taken from the size of the
-#' input data. The counts are preprocessed using \code{\link{pre_proc_data}} and
-#' then parameters are estimated using \code{\link{sparsedc_cluster}} using
-#' lambda values calculated using \code{\link{lambda1_calculator}} and
-#' \code{\link{lambda2_calculator}}.
+#' input data. The counts are preprocessed using
+#' \code{\link[SparseDC]{pre_proc_data}} and then parameters are estimated using
+#' \code{\link[SparseDC]{sparsedc_cluster}} using lambda values calculated using
+#' \code{\link[SparseDC]{lambda1_calculator}} and
+#' \code{\link[SparseDC]{lambda2_calculator}}.
 #'
-#' See \code{\link{SparsDCParams}} for more details on the parameters.
+#' See \code{\link{SparseDCParams}} for more details on the parameters.
 #'
 #' @return SparseParams object containing the estimated parameters.
 #'
@@ -87,8 +88,8 @@ sparseDCEstimate.matrix <- function(counts, conditions, nclusters, norm = TRUE,
                         nCells = round(ncol(counts) / 2),
                         markers.n = markers.n,
                         markers.shared = markers.n - markers.diff,
-                        clusts.c1 = sort(unique(sdc_res$clusters1)),
-                        clusts.c2 = sort(unique(sdc_res$clusters2)))
+                        clusts.c1 = sort(unique(sdc.res$clusters1)),
+                        clusts.c2 = sort(unique(sdc.res$clusters2)))
 
     return(params)
 }

R/sparseDC-simulate.R

@@ -9,7 +9,7 @@
 #'
 #' @details
 #' This function is just a wrapper around
-#' \code{\link[SparsDC]{sim_data}} that takes a
+#' \code{\link[SparseDC]{sim_data}} that takes a
 #' \code{\link{SparseDCParams}}, runs the simulation then converts the
 #' output from log-expression to counts and returns a
 #' \code{\link[SingleCellExperiment]{SingleCellExperiment}} object. The original

man/SparseDCParams.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/AllClasses.R
+\docType{class}
+\name{SparseDCParams}
+\alias{SparseDCParams}
+\alias{SparseDCParams-class}
+\title{The SparseDCParams class}
+\description{
+S4 class that holds parameters for the SparseDC simulation.
+}
+\section{Parameters}{
+
+
+The SparseDC simulation uses the following parameters:
+
+\describe{
+    \item{\code{nGenes}}{The number of genes to simulate in each condition.}
+    \item{\code{nCells}}{The number of cells to simulate.}
+    \item{\code{[seed]}}{Seed to use for generating random numbers.}
+    \item{\code{markers.n}}{Number of marker genes to simulate for each
+    cluster.}
+    \item{\code{markers.shared}}{Number of marker genes for each cluster
+    shared between conditions. Must be less than or equal to
+    \code{markers.n}}.
+    \item{\code{[markers.same]}}{Logical. Whether each cluster should have
+    the same set of marker genes.}
+    \item{\code{clusts.c1}}{Numeric vector of clusters present in
+    condition 1. The number of times a cluster is repeated controls the
+    proportion of cells from that cluster.}
+    \item{\code{clusts.c2}}{Numeric vector of clusters present in
+    condition 2. The number of times a cluster is repeated controls the
+    proportion of cells from that cluster.}
+    \item{\code{[mean.lower]}}{Lower bound for cluster gene means.}
+    \item{\code{[mean.upper]}}{Upper bound for cluster gene means.}
+}
+
+The parameters not shown in brackets can be estimated from real data using
+\code{\link{sparseDCEstimate}}. For details of the SparseDC simulation
+see \code{\link{sparseDCSimulate}}.
+}
+

man/newParams.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/AllGenerics.R, R/BASiCSParams-methods.R,
 %   R/Lun2Params-methods.R, R/LunParams-methods.R, R/MFAParams-methods.R,
 %   R/PhenoParams-methods.R, R/SCDDParams-methods.R, R/SimpleParams-methods.R,
-%   R/SplatParams-methods.R, R/ZINBParams-methods.R
+%   R/SparseDCParams-methods.R, R/SplatParams-methods.R, R/ZINBParams-methods.R
 \name{newParams}
 \alias{newParams}
 \alias{newBASiCSParams}
@@ -12,6 +12,7 @@
 \alias{newPhenoParams}
 \alias{newSCDDParams}
 \alias{newSimpleParams}
+\alias{newSparseDCParams}
 \alias{newSplatParams}
 \alias{newZINBParams}
 \title{New Params}
@@ -30,6 +31,8 @@ newSCDDParams(...)
 
 newSimpleParams(...)
 
+newSparseDCParams(...)
+
 newSplatParams(...)
 
 newZINBParams(...)

man/phenoSimulate.Rd

@@ -26,7 +26,7 @@ This function is just a wrapper around
 \code{\link{PhenoParams}}, runs the simulation then converts the
 output from log-expression to counts and returns a
 \code{\link[SingleCellExperiment]{SingleCellExperiment}} object. The original
-simulated log-expression values are returned in the \code{LogExprs} asssay.
+simulated log-expression values are returned in the \code{LogExprs} assay.
 See \code{\link[phenopath]{simulate_phenopath}} and the PhenoPath paper for
 more details about how the simulation works.
 }

man/sparseDCEstimate.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sparseDC-estimate.R
+\name{sparseDCEstimate}
+\alias{sparseDCEstimate}
+\alias{sparseDCEstimate.SingleCellExperiment}
+\alias{sparseDCEstimate.matrix}
+\title{Estimate SparseDC simulation parameters}
+\usage{
+sparseDCEstimate(counts, conditions, nclusters, norm = TRUE,
+  params = newSparseDCParams())
+
+\method{sparseDCEstimate}{SingleCellExperiment}(counts, conditions, nclusters,
+  norm = TRUE, params = newSparseDCParams())
+
+\method{sparseDCEstimate}{matrix}(counts, conditions, nclusters, norm = TRUE,
+  params = newSparseDCParams())
+}
+\arguments{
+\item{counts}{either a counts matrix or an SingleCellExperiment object
+containing count data to estimate parameters from.}
+
+\item{conditions}{numeric vector giving the condition each cell belongs to.}
+
+\item{nclusters}{number of cluster present in the dataset.}
+
+\item{norm}{logical, whether to libray size normalise counts before
+estimation. Set this to FALSE if counts is already normalised.}
+
+\item{params}{PhenoParams object to store estimated values in.}
+}
+\value{
+SparseParams object containing the estimated parameters.
+}
+\description{
+Estimate simulation parameters for the SparseDC simulation from a real
+dataset.
+}
+\details{
+The \code{nGenes} and \code{nCells} parameters are taken from the size of the
+input data. The counts are preprocessed using
+\code{\link[SparseDC]{pre_proc_data}} and then parameters are estimated using
+\code{\link[SparseDC]{sparsedc_cluster}} using lambda values calculated using
+\code{\link[SparseDC]{lambda1_calculator}} and
+\code{\link[SparseDC]{lambda2_calculator}}.
+
+See \code{\link{SparseDCParams}} for more details on the parameters.
+}
+\examples{
+# Load example data
+library(scater)
+data("sc_example_counts")
+
+conditions <- sample(1:2, ncol(sc_example_counts), replace = TRUE)
+
+params <- sparseDCEstimate(sc_example_counts[1:500, ], conditions,
+                           nclusters = 3)
+params
+}

man/sparseDCSimulate.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sparseDC-simulate.R
+\name{sparseDCSimulate}
+\alias{sparseDCSimulate}
+\title{SparseDC simulation}
+\usage{
+sparseDCSimulate(params = newSparseDCParams(), verbose = TRUE, ...)
+}
+\arguments{
+\item{params}{SparseDCParams object containing simulation parameters.}
+
+\item{verbose}{logical. Whether to print progress messages}
+
+\item{...}{any additional parameter settings to override what is provided in
+\code{params}.}
+}
+\value{
+SingleCellExperiment containing simulated counts
+}
+\description{
+Simulate counts from cluster in two conditions using the SparseDC method.
+}
+\details{
+This function is just a wrapper around
+\code{\link[SparseDC]{sim_data}} that takes a
+\code{\link{SparseDCParams}}, runs the simulation then converts the
+output from log-expression to counts and returns a
+\code{\link[SingleCellExperiment]{SingleCellExperiment}} object. The original
+simulated log-expression values are returned in the \code{LogExprs} assay.
+See \code{\link[SparseDC]{sim_data}} and the SparseDC paper for
+more details about how the simulation works.
+}
+\examples{
+sim <- sparseDCSimulate()
+
+}
+\references{
+Campbell K, Yau C. Uncovering genomic trajectories with heterogeneous genetic
+and environmental backgrounds across single-cells and populations. bioRxiv
+(2017).
+
+Barron M, Zhang S, Li J. A sparse differential clustering algorithm for
+tracing cell type changes via single-cell RNA-sequencing data.
+Nucleic Acids Research (2017).
+
+Paper: \url{10.1093/nar/gkx1113}
+}