removed typo and added another

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mnem
 Type: Package
 Title: Mixture Nested Effects Models
-Version: 0.99.8
+Version: 0.99.9
 Author: Martin Pirkl
 Maintainer: Martin Pirkl <martin.pirkl@bsse.ethz.ch>
 Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

vignettes/mnem.Rmd

@@ -403,7 +403,8 @@ over-fitting.
 The data object 'app' consists of three lists containing the results
 for CROPSeq, PERTURBSeq (transcription factors) and PERTURBSeq
 (cell cycle regulators). Each list contains the results for
-$k=1,2,3,4,5$ (number of components).
+$k=1,2,3,4,5$ (number of components). See section 'Data generation'
+for details.
 
 For each of the three data sets we show the raw log likelihood
 together with the penalized likelihood. We choose the optimal $k$ at
@@ -444,7 +445,7 @@ for (j in 1:3) {
 ```
 Figure 8 shows that for each data set, the optimal mixture is $k=2$ according to
 our penalized log likelihood. However, while for the first and second
-data set even $k=2$ beats just a single network, for the third data set
+data set even $k=3$ beats just a single network, for the third data set
 there is only weak evidence for a mixture of $k>1$.
 
 Each cell has a certain probability (responsibility) to have been
@@ -595,9 +596,10 @@ Perturb-seq: Dissecting molecular circuits with scalable single-cell rna
 profiling of pooled genetic screens.  
 Cell, 167(7), 1853???1866.e17.
 
-# Generating the data objects
+# Data Generation
 
-The R code for generating the data objects is shown in the accompanying R file.
+The R code for generating the data objects 'sim' and 'app' is shown
+in the accompanying R file.
 
 ```{r, eval=FALSE, include=FALSE}
 data <- read.csv("GSE92872_CROP-seq_Jurkat_TCR.digital_expression.csv",