updated

Author: software-github

R/.DS_Store

@@ -0,0 +1,2 @@
+^.*\.Rproj$
+^\.Rproj\.user$

R/.Rbuildignore

@@ -1,10 +1,10 @@
 Package: SCRABBLE
 Type: Package
-Title: Impute scRNAseq data method
+Title: Impute scRNAseq Data Method
 Version: 0.0.1
 Author: Tao Peng
 Maintainer: The package maintainer Tao Peng <software.github@gmail.com>
-Description: SCRABBLE imputes drop-out data by optimizing an objective function 
+Description: It aims to impute drop-out data by optimizing an objective function 
    that consists of three terms. The first term ensures that imputed values for 
    genes with nonzero expression remain as close to their original values as possible, 
    thus minimizing unwanted bias towards expressed genes. The second term ensures the 
@@ -23,8 +23,10 @@ Imports: Rcpp (>= 0.12.13),
          ggplot2, 
          RColorBrewer,
          reshape2,
-         gridExtra
-LinkingTo: Rcpp, RcppEigen, gridExtra
+         gridExtra,
+         methods,
+         grDevices
+LinkingTo: Rcpp, RcppEigen
 RoxygenNote: 6.1.1
 Suggests: knitr,rmarkdown,BiocStyle
 VignetteBuilder: knitr

R/DESCRIPTION

@@ -10,3 +10,5 @@ import(ggplot2)
 import(RColorBrewer)
 import(reshape2)
 import(gridExtra)
+import(methods)
+import(grDevices)

R/NAMESPACE

@@ -1,13 +1,32 @@
 # plot the figures of data
+#'
+#' It is used to plot the data as a images
+#'
+#' @param data the input data which is a data matrix.
+#'
+#' @param name the name of the data and it is a string.
+#'
+#'
+#' @examples
+#' # Run plot_data
+#' p <- plot_data(demo_data[[1]],"Drop-out Data")
+#'
+#' @return ggplot handler
+#'
+#' @export
+#'
+#'
+
 plot_data <- function(data,name){
   limit <- c(0,5)
   myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
   print(dim(data))
   colnames(data) <- NULL
   rownames(data) <- NULL
   longData<-melt(as.matrix(data))
-  pl <- ggplot(longData, aes(x = Var2, y = Var1)) +
-    geom_raster(aes(fill=value)) +
+  colnames(longData) <- c("Var1", "Var2","value")
+  pl <- ggplot(longData, aes_string(x = "Var2", y = "Var1")) +
+    geom_raster(aes_string(fill= "value")) +
     scale_colour_gradient2(limits=c(0, 5)) +
     scale_fill_gradientn(colours = c("white", "blue", "red"), values = c(0,0.6,1)) +
     theme_bw()  +

R/R/.DS_Store

@@ -1,12 +1,12 @@
 #' Runs SCRABBLE
 #'
-#' SCRABBLE imputes drop-out data by optimizing an objective function that consists of three terms.
+#' This package imputes drop-out data by optimizing an objective function that consists of three terms.
 #' The first term ensures that imputed values for genes with nonzero expression remain as close to their
 #' original values as possible, thus minimizing unwanted bias towards expressed genes. The second term ensures
 #' the rank of the imputed data matrix to be as small as possible. The rationale is that we only expect a
 #' limited number of distinct cell types in the samples. The third term operates on the bulk RNA-Seq data.
 #' It ensures consistency between the average gene expression of the aggregated imputed data and the
-#' average gene expression of the bulk RNA-Seq data. We developed 58 a convex optimization algorithm to minimize
+#' average gene expression of the bulk RNA-Seq data. We developed a convex optimization algorithm to minimize
 #' the objective function.
 #'
 #'
@@ -31,7 +31,7 @@
 #' parameter <- c(1, 1e-6, 1e-4)
 #'
 #' # Run SCRABLE
-#' result <- scrabble(data,parameter = parameter)
+#' result <- scrabble(demo_data,parameter = parameter)
 #'
 #' @return A data matrix with the same size of the input scRNAseq data
 #'