Fix functions and Vignettes. update to 0.6.0

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: scaffold
 Type: Package
 Title: Simulation framework that models each step of a single-cell RNA-seq experiment
-Version: 0.5.0
+Version: 0.6.0
 Author: Rhonda Bacher
 Maintainer: Rhonda Bacher <rbacher@ufl.edu>
 Description: This package implements Scaffold — a method to simulate scRNA-seq data.
@@ -10,12 +10,9 @@ LazyData: true
 Encoding: UTF-8
 Imports:
     wrswoR,
-    parallel,
-    MASS,
     SingleCellExperiment,
     yarrr,
     methods,
-    Rcpp,
     splines,
     Rfast,
     data.table,

NAMESPACE

@@ -1,7 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
+export(captureStep)
 export(estimateScaffoldParameters)
+export(generateDynamicGeneCounts)
 export(makePlots)
+export(sequenceStep10X)
+export(sequenceStepC1)
 export(simulateScaffold)
 exportClasses(ScaffoldParams)
 import(data.table)

R/amplification.R

@@ -1,4 +1,11 @@
 #' Pre-amplification step
+#' @param capturedMolecules A truncated lists of genes.
+#' @param genes A vector of names for each gene. If left NULL, the gene names from the \code{sce} object are used.
+#' @param efficiencyPCR A numeric vector representing the efficiency of PCR for each sample.
+#' @param rounds An integer specifying the number of PCR or IVT amplification rounds to perform.
+#' @param typeAMP The amplification method used in the simulation, defaults to "PCR", "IVT" is another accepted value.
+#' @param useUMI A TRUE/FALSE indicating whether the protocol should use UMIs (Unique Molecular Identifiers). Droplet or 10X protocols have this set as TRUE for the default, otherwise FALSE.
+#' 
 #' @importFrom Rfast Table rep_col
 preamplifyStep <- function(capturedMolecules, genes, efficiencyPCR, rounds, typeAMP, useUMI){
 
@@ -24,6 +31,11 @@ preamplifyStep <- function(capturedMolecules, genes, efficiencyPCR, rounds, type
 }
 
 #' The second amplification step
+#' @param capturedMolecules A truncated lists of genes.
+#' @param genes A vector of names for each gene. If left NULL, the gene names from the \code{sce} object are used.
+#' @param efficiencyPCR A numeric vector representing the efficiency of PCR for each sample.
+#' @param rounds An integer specifying the number of PCR or IVT amplification rounds to perform.
+#' @param protocol The protocol to model in the simulation (accepted input is: C1, droplet, 10X).
 amplifyStep <- function(capturedMolecules, genes, efficiencyPCR, rounds, protocol){
 
   if (protocol == "C1")

R/capture.R

@@ -1,5 +1,13 @@
 #' Capture cDNA from cell (cell lysis and conversion of mRNA to cDNA)
+#' @param Data A rounded numeric matrix of gene expression counts.
+#' @param captureEffCell A vector of values between 0 and 1 to indicate the proportion of mRNA molecules successfully captured for the genes in each cell.
+#' @param captureEffGene A vector of values between 0 and 1. If left NULL, all genes are assumed to have equal capture efficiency, and the vector is normalized to sum to 1.
+#' @param rtEffCell A vector of values between 0 and 1 to indicate the proportion of mRNA succesfully converted to cDNA.
+#' @param rtEffGene A vector with values normalized to sum to 1.
+#' @param useUMI A TRUE/FALSE indicating whether the protocol should use UMIs (Unique Molecular Identifiers). Droplet or 10X protocols have this set as TRUE for the default, otherwise FALSE.
+#' 
 #' @importFrom wrswoR sample_int_rej
+#' @export
 captureStep <- function(Data, captureEffCell = NULL, captureEffGene = NULL, 
                         rtEffCell = NULL, rtEffGene = NULL, useUMI = FALSE){
 

R/initialCounts.R

@@ -24,8 +24,10 @@ generateGeneCounts <- function(numCells, mu, popHet) {
 ## The motivation for this code is from the simstudy package
 #' @inheritParams generateGeneCounts
 #' @inheritParams estimateScaffoldParameters
+#' @param dynamicParams This should be a named list with elements: propGenes, dynGenes, degree, knots, and theta. propGenes indicates the proportion of genes that should be simulated dynamic. dynGenes is an optional parameter detailing an exact list of genes that will be generated as dynamic. degree, knots, and theta control the spline parameters to generate dynamic trends.
 #' @importFrom splines bs
 #' @importFrom data.table data.table
+#' @export
 generateDynamicGeneCounts <- function(numCells, mu, dynamicParams) {
 
   if (is.null(dynamicParams$dynGenes)) {