diff --git a/DESCRIPTION b/DESCRIPTION
index 530d696732d9a2b63f192c508f7407d239ff03b6..3ef758f6696565391eb13544142c7a0b347c100f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: splatter
 Type: Package
 Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 0.99.9
-Date: 2017-02-02
+Version: 0.99.10
+Date: 2017-03-07
 Author: Luke Zappia
 Authors@R:
     c(person("Luke", "Zappia", role = c("aut", "cre"),
diff --git a/NEWS.md b/NEWS.md
index 43207e0429ec2c06975707360281973d7eab0fd5..9480f9171c1184640bb042a31ba60ee3454e50c6 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,10 @@
+# 0.99.10
+
+* Improve Splat estimation procedure
+* Update default Splat parameters
+* Remove out.loProb Splat parameter
+* Add MeanZeros plot to compareSCESets
+
 # 0.99.9
 
 * Add addGeneLengths function
diff --git a/R/compare.R b/R/compare.R
index 82f26583831f18b85b18a5b3076be6d410d06f0b..989417a0b263d5d3f145ab995e32f25b0f7c6cac 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -24,6 +24,8 @@
 #'             that is zero.}
 #'             \item{\code{ZerosCell}}{Boxplot of the percentage of each cell
 #'             that is zero.}
+#'             \item{\code{MeanZeros}}{Scatter plot with fitted lines showing
+#'             the mean-dropout relationship.}
 #'     }
 #'   }
 #' }
@@ -95,8 +97,8 @@ compareSCESets <- function(sces) {
 
     mean.var <- ggplot(fData.all,
                        aes_string(x = "mean_log_cpm", y = "var_log_cpm",
-                                  olour = "Dataset", fill = "Dataset")) +
-        geom_point() +
+                                  colour = "Dataset", fill = "Dataset")) +
+        geom_point(alpha = 0.2) +
         geom_smooth() +
         xlab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
         ylab(expression(paste("Variance ", log[2], "(CPM + 1)"))) +
@@ -130,6 +132,16 @@ compareSCESets <- function(sces) {
         ggtitle("Distribution of zeros per cell") +
         theme_minimal()
 
+    mean.zeros <- ggplot(fData.all,
+                         aes_string(x = "mean_log_cpm", y = "pct_dropout",
+                                    colour = "Dataset", fill = "Dataset")) +
+        geom_point(alpha = 0.2) +
+        geom_smooth() +
+        xlab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
+        ylab(expression(paste("Percentage zeros"))) +
+        ggtitle("Mean-dropout relationship") +
+        theme_minimal()
+
     comparison <- list(FeatureData = fData.all,
                        PhenoData = pData.all,
                        Plots = list(Means = means,
@@ -137,7 +149,8 @@ compareSCESets <- function(sces) {
                                     MeanVar = mean.var,
                                     LibrarySizes = libs,
                                     ZerosGene = z.gene,
-                                    ZerosCell = z.cell))
+                                    ZerosCell = z.cell,
+                                    MeanZeros = mean.zeros))
 
     return(comparison)
 }
diff --git a/man/compareSCESets.Rd b/man/compareSCESets.Rd
index b66c525475e8d11a35569dee1615c2226f1d8caa..14eb59dca842ebf7e10f78a612da9155b6b73a8c 100644
--- a/man/compareSCESets.Rd
+++ b/man/compareSCESets.Rd
@@ -35,6 +35,8 @@ The return list has three items:
             that is zero.}
             \item{\code{ZerosCell}}{Boxplot of the percentage of each cell
             that is zero.}
+            \item{\code{MeanZeros}}{Scatter plot with fitted lines showing
+            the mean-dropout relationship.}
     }
   }
 }