Version bumped 0.99.1

TanerArslan · Mar 3, 2019 · 4a3299a · 4a3299a
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Showing 4 changed files with 31 additions and 14 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SubCellBarCode
 Type: Package
 Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome
-Version: 0.99.0
+Version: 0.99.1
 Author: Taner Arslan
 Maintainer: Taner Arslan <taner.arslan@ki.se>
 Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization.

diff --git a/R/.Rapp.history b/R/.Rapp.history
diff --git a/R/tSNEVisualization.R b/R/tSNEVisualization.R
@@ -56,11 +56,20 @@ tsneVisualization <- function(protein.data, markerProteins, dims,
 
         message("Optimization was performed.")
 
+        #get the optimization parameters
+        theta.val <- as.numeric(paste(0,
+                        as.character(min.theta.perp[2]), sep = "."))
+
+        perplexity.val <- as.numeric(min.theta.perp[1])
+
+        message(sprintf("Theta value: %s", theta.val))
+        message(sprintf("Perplexity value: %s", perplexity.val))
+
+
         rtsne.map <- Rtsne::Rtsne(tsne.df,
                         dims = 3,
-                        theta=as.numeric(paste(0,
-                            as.character(min.theta.perp[2]), sep = ".")),
-                        perplexity=as.numeric(min.theta.perp[1]))
+                        theta= theta.val,
+                        perplexity = perplexity.val)
 
 
         #plot 3D tsne-map
@@ -77,7 +86,7 @@ tsneVisualization <- function(protein.data, markerProteins, dims,
                 legend = c("S1", "S2", "S3", "S4","N1", "N2", "N3", "N4",
                             "C1", "C2", "C3", "C4", "C5", "M1", "M2"),
                 pch = 16,
-                cex = 0.85,
+                cex = 0.75,
                 col = c("gold", "orange", "salmon", "tomato2",
                         "grey90","grey70", "grey50", "grey30",
                         "lightblue", "aquamarine", "cyan", "deepskyblue2",
@@ -92,7 +101,7 @@ tsneVisualization <- function(protein.data, markerProteins, dims,
                 legend = c("S1", "S2", "S3", "S4","N1", "N2", "N3", "N4",
                             "C1", "C2", "C3", "C4", "C5", "M1", "M2"),
                 pch = 16,
-                cex = 0.85,
+                cex = 0.75,
                 col = c("gold", "orange", "salmon", "tomato2",
                         "grey90", "grey70", "grey50", "grey30",
                         "lightblue","aquamarine", "cyan","deepskyblue2",
@@ -107,7 +116,7 @@ tsneVisualization <- function(protein.data, markerProteins, dims,
                 legend = c("S1", "S2", "S3", "S4","N1", "N2", "N3", "N4",
                             "C1", "C2", "C3", "C4", "C5", "M1", "M2"),
                 pch = 16,
-                cex = 0.85,
+                cex = 0.75,
                 col = c("gold", "orange", "salmon", "tomato2",
                         "grey90", "grey70", "grey50", "grey30",
                         "lightblue", "aquamarine", "cyan", "deepskyblue2",
@@ -135,11 +144,18 @@ tsneVisualization <- function(protein.data, markerProteins, dims,
 
         message("Optimization was performed.")
 
+        #get the optimization parameters
+        theta.val <- as.numeric(paste(0,
+                        as.character(min.theta.perp[2]), sep = "."))
+
+        perplexity.val <- as.numeric(min.theta.perp[1])
+
+        message(sprintf("Theta value: %s", theta.val))
+        message(sprintf("Perplexity value: %s", perplexity.val))
         rtsne.map <- Rtsne::Rtsne(tsne.df,
                         dims = 2,
-                        theta=as.numeric(paste(0,
-                            as.character(min.theta.perp[2]), sep = ".")),
-                        perplexity=as.numeric(min.theta.perp[1]))
+                        theta = theta.val,
+                        perplexity = perplexity.val)
 
         #plot 2D tsne-map
         d <- data.frame(x=rtsne.map$Y[, 1],

diff --git a/vignettes/SubCellBarCode.Rmd b/vignettes/SubCellBarCode.Rmd
@@ -151,6 +151,7 @@ distribution and separation of marker proteins.
 
 ```{r tsneparameter}
 #Default parameters
+#Run the broad-range parameters to optimize well !!!
 #Output dimensionality
 #dims = 3
 #Speed/accuracy trade-off (increase for less accuracy) 
@@ -171,8 +172,8 @@ set.seed(6)
 tsne.map <- tsneVisualization(protein.data = df, 
                     markerProteins = r.markers, 
                     dims = 3, 
-                    theta = c(0.1, 0.2, 0.3, 0.4, 0.5), 
-                   perplexity = c(5, 10, 20, 30, 40, 50, 60)) 
+                    theta = c(0.1), 
+                    perplexity = c(60)) 
 ```
 
 We recommend 3D vizualisation by setting `dims = 3`, 
@@ -188,8 +189,8 @@ set.seed(9)
 tsne.map2 <- tsneVisualization(protein.data = df, 
                     markerProteins = r.markers, 
                     dims = 2, 
-                    theta = c(0.1, 0.2, 0.3, 0.4, 0.5), 
-                    perplexity = c(5, 10, 20, 30, 40, 50, 60))
+                    theta = c(0.5), 
+                    perplexity = c(60))
 ```
 
 ## Build model and classify proteins