Merge branch 'release/v0.0.4'

Author: drisso

DESCRIPTION

@@ -1,20 +1,20 @@
 Package: scone
-Version: 0.0.3
+Version: 0.0.4
 Title: Single Cell Overview of Normalized Expression data
 Description: scone is a package to compare and rank the performance of different normalization schemes in real single-cell RNA-seq datasets.
 Authors@R: c(person("Michael", "Cole", email = "mbeloc@gmail.com",
 	          role = c("aut", "cre", "cph")),
 	     person("Davide", "Risso", email = "risso.davide@gmail.com",
 	          role = c("aut")))
-Author: Michael Cole [aut, cre, cph] and Davide Risso [aut]
+Author: Michael Cole [aut, cre, cph], Davide Risso [aut]
 Maintainer: Michael Cole <mbeloc@gmail.com>
-Date: 2016-02-14
+Date: 2016-05-12
 License: Artistic-2.0
 Depends:
-  R (>= 3.1)
+  R (>= 3.3)
 Imports:
   BiocParallel,
-  clusterCells,
+  cluster,
   DESeq,
   EDASeq,
   MASS,
@@ -28,7 +28,7 @@ Imports:
   limma,
   matrixStats,
   mixtools,
-  scde
+  grDevices
 Suggests:
   knitr,
   rmarkdown,

NAMESPACE

@@ -2,6 +2,7 @@
 
 export(DESEQ_FN)
 export(DESEQ_FN_POS)
+export(FQT_FN)
 export(FQ_FN)
 export(FQ_FN_POS)
 export(TMM_FN)
@@ -26,12 +27,13 @@ importFrom(MASS,glm.nb)
 importFrom(RUVSeq,RUVg)
 importFrom(aroma.light,normalizeQuantileRank.matrix)
 importFrom(class,knn)
-importFrom(clusterCells,subsampleClustering)
+importFrom(cluster,silhouette)
 importFrom(diptest,dip.test)
 importFrom(edgeR,calcNormFactors)
 importFrom(fpc,pamk)
 importFrom(grDevices,colorRampPalette)
 importFrom(limma,lmFit)
+importFrom(matrixStats,colIQRs)
+importFrom(matrixStats,colMedians)
 importFrom(matrixStats,rowMedians)
 importFrom(mixtools,normalmixEM)
-importFrom(scde,bwpca)

R/SCONE_DEFAULTS.R

@@ -20,7 +20,7 @@ uq_f <- function(which="upper", round = FALSE){
 #            exprs(x) <- UQ_FN(exprs(x))
 #            return(x)
 #          })
-# 
+#
 # setMethod(f="UQ_FN",
 #           signature="matrix",
 #           definition= function(ei){
@@ -52,15 +52,23 @@ FQ_FN = function(ei){
   return(eo)
 }
 
+#' @rdname FQ_FN
+#' @details FQT_FN handles ties carefully (see \code{\link[limma]{normalizeQuantiles}}).
+#' @export
+FQT_FN = function(ei){
+  eo = normalizeQuantileRank.matrix(ei, ties = TRUE)
+  return(eo)
+}
+
 #' Full-Quantile normalization applied to positive data.
 #' @export
 #' @param ei = Numerical matrix. (rows = genes, cols = samples). Unique row.names are required.
 #' @return FQ (positive) normalized matrix.
 FQ_FN_POS = function(ei){
-  
+
   # Vector of integers used for computation
   base_rank = 1:nrow(ei)
-  
+
   # Quantile Index Matrix: Values between 0 and 1 corresponding to quantile
   quant_mat = NULL
   # Re-ordered Data Matrix
@@ -75,7 +83,7 @@ FQ_FN_POS = function(ei){
     quant[quant > 1] = NA
     quant_mat = cbind(quant_mat,quant)
   }
-  
+
   # Vector form of quantile index matrix
   quant_out = as.numeric(as.vector(quant_mat))
   # Interpolation Matrix (Values of all quantiles)
@@ -95,7 +103,7 @@ FQ_FN_POS = function(ei){
 
   # Average over the interpolated values from all samples
   inter_mean = inter_mat/ob_counts
-  
+
   ## Substituting Mean Interpolated Values for Expression Values and Return
   eo = matrix(inter_mean,ncol = dim(ei)[2])
   eo[is.na(eo)] = 0
@@ -136,7 +144,7 @@ DESEQ_FN_POS = function(ei){
   }
   if(!any(geom_mean > 0)){
     stop("Geometric mean non-positive for all genes.")
-  } 
+  }
   ratios = ei / geom_mean # Divide each Expression Value by Geometric Mean of Corresponding Gene
   if(any(is.infinite(geom_mean))){
     stop("Infinite mean! This should never happen :-<")

R/biplot.R

@@ -46,4 +46,6 @@ biplot_colored <- function(x, y, choices=1:2, expand=1, ...) {
 
   text(yy/ratio, labels=labs, col=2)
   arrows(0, 0, yy[, 1] * 0.8/ratio, yy[, 2] * 0.8/ratio, col = 2, length = 0.1)
+
+  invisible(xx)
 }