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R/SPsimSeq-package.R

@@ -1,17 +1,20 @@
 #' SPsimSeq package
 #'
-#'
 #' SPsimSeq uses a specially designed exponential family for density estimation 
 #' to constructs the distribution of gene expression levels from a given real RNA 
 #' sequencing data (single-cell or bulk), and subsequently, simulates a new 
 #' dataset from the estimated marginal distributions using Gaussian-copulas to 
 #' retain the dependence between genes. It allows simulation of multiple 
 #' groups and batches with any required sample size and library size.
 #'
-#'
 #' @docType package
 #' @author  Alemu Takele Assefa \email{alemutak@hotmail.com}
 #' @author Stijn Hawinkel \email{stijnhawinkel@hotmail.com}
+#' 
+#' @references
+#' \itemize{
+#' \item Alemu Takele Assefa, Jo Vandesompele, Olivier Thas. (2020). SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data, \emph{Bioinformatics}, , btaa105, https://doi.org/10.1093/bioinformatics/btaa105
+#' }
 #' @name SPsimSeq-package  
 #' 
 NULL

README.Rmd

@@ -7,7 +7,7 @@ This is the github repo for the SPsimSeq R package.
 
 # Overview
 SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently, simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.
- 
+
 # Installation
 
 Github installation
@@ -98,4 +98,5 @@ class(sim.data.sc1)
 head(counts(sim.data.sc1)[, seq_len(5)])
 colData(sim.data.sc1)
 rowData(sim.data.sc1)
-```
+```
+