Final fixes to the vignette

Tutorial_HTM_2016.Rmd

 ---
 title: "Visual Exploration of High-Throughput-Microscopy Data"
-author: "Bernd Klaus, Andrzej Oles, Mike Smith"
+author: "Bernd Klaus, Andrzej Oleś, Mike Smith"
 date: "`r doc_date()`"
 bibliography: HTM_2016.bib
 output: 
@@ -59,7 +59,6 @@ each single cell. These classification results have been obtained using a machin
 learning algorithm based on the original image features. The data produced is similar
 to the one in @Neumann_2010: Each cell is classified into a mitotic phenotype class.
 
-<!-- ![96_well_plate.jpeg](96_well_plate.jpeg) -->
 
 # Annotation import
 
@@ -77,7 +76,7 @@ head(plate_map)
 # Importing the raw data
 
 We will now import the raw data. This data is stored in a variant of the [HDF5 format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) called
-["CellH5"](http://www.cellh5.org/),
+"[CellH5](http://www.cellh5.org/)",
 which defines a more restricted sub-format designed specifically to store data
 from high content screens. More information can be found in the paper by 
 @Sommer_2013.
@@ -106,7 +105,7 @@ the screen plate in the columns and the counts for the respective classes in the
 rows.
 
 This is a typical example of a "wide" data table, where the variables 
-contained in the data set spread across multiple columns. 
+contained in the data set spread across multiple columns (here we only show the first six ones). 
 
 ```{r import_data_table, dependson="readCellH5"}
 raw_data <- sapply(c5_pos,
@@ -115,7 +114,7 @@ raw_data <- sapply(c5_pos,
                       table(predictions)
                     })                     
 
-head(raw_data)
+raw_data[, 1:6]
 ```
 
 
@@ -496,7 +495,7 @@ assembled in the R object `DNase`, which conveniently comes with base R.
 `conc`, the protein concentration that was used; and `density`, the
 measured optical density.
 
-```{r figredobasicplottingwithggplot, fig.width = 3.5, fig.height = 5}
+```{r figredobasicplottingwithggplot, fig.width = 6, fig.height = 9}
 ggplot(DNase, aes(x = conc, y = density, color = Run)) + geom_point() 
 ```
 
@@ -507,7 +506,7 @@ Then we told `ggplot` via the aesthetics `aes` argument which variables
 we want on the $x$- and $y$-axes, respectively and mapped the run number 
 to the color aesthetic.
 Finally, we stated that we want the plot to use points, by adding the result 
-of calling the function `geom\_point`. 
+of calling the function `geom_point`. 
 
 ## Principal component analysis (PCA) to for data visualization