diff --git a/Tutorial_HTM_2016.Rmd b/Tutorial_HTM_2016.Rmd
index 7cfcea7c6ec1eea75912f4fa3e6ca7a4b3971727..8f92f21294ffcb1addbff42e965b306460789cb6 100755
--- a/Tutorial_HTM_2016.Rmd
+++ b/Tutorial_HTM_2016.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "Visual Exploration of High-Throughput-Microscopy Data"
-author: "Bernd Klaus, Andrzej Oles, Mike Smith"
+author: "Bernd Klaus, Andrzej OleÅ›, 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
 
diff --git a/Tutorial_HTM_2016.html b/Tutorial_HTM_2016.html
index 3cd31006b938729dfcca467bd69ce473d2c1a7b6..43c8a0dc8fd153802876de8e909c5261e5c8a4b7 100644
--- a/Tutorial_HTM_2016.html
+++ b/Tutorial_HTM_2016.html
@@ -8,7 +8,7 @@
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="pandoc" />
 
-<meta name="author" content="Bernd Klaus, Andrzej Oles, Mike Smith" />
+<meta name="author" content="Bernd Klaus, Andrzej OleÅ›, Mike Smith" />
 
 <meta name="date" content="2016-10-22" />
 
@@ -62,7 +62,7 @@ document.addEventListener("DOMContentLoaded", function() {
 
 <div id="header">
 <h1 class="title">Visual Exploration of High-Throughput-Microscopy Data</h1>
-<h4 class="author"><em>Bernd Klaus, Andrzej Oles, Mike Smith</em></h4>
+<h4 class="author"><em>Bernd Klaus, Andrzej OleÅ›, Mike Smith</em></h4>
 <h4 class="date"><em>22 October 2016</em></h4>
 </div>
 
@@ -111,7 +111,6 @@ rmarkdown::render('Tutorial_HTM_2016.Rmd', BiocStyle::pdf_document())
 <div id="about-the-tutorial" class="section level1">
 <h1><span class="header-section-number">2</span> About the tutorial</h1>
 <p>In this tutorial, we will import a single plate from a high content screen performed on 96 well plates. The input data that we are going to use are class labels for each single cell. These classification results have been obtained using a machine learning algorithm based on the original image features. The data produced is similar to the one in <span class="citation">Neumann et al. (2010)</span>: Each cell is classified into a mitotic phenotype class.</p>
-<!-- ![96_well_plate.jpeg](96_well_plate.jpeg) -->
 </div>
 <div id="annotation-import" class="section level1">
 <h1><span class="header-section-number">3</span> Annotation import</h1>
@@ -128,7 +127,7 @@ rmarkdown::render('Tutorial_HTM_2016.Rmd', BiocStyle::pdf_document())
 </div>
 <div id="importing-the-raw-data" class="section level1">
 <h1><span class="header-section-number">4</span> Importing the raw data</h1>
-<p>We will now import the raw data. This data is stored in a variant of the <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> called <a href="http://www.cellh5.org/">“CellH5”</a>, 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 <span class="citation">Sommer et al. (2013)</span>.</p>
+<p>We will now import the raw data. This data is stored in a variant of the <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> called “<a href="http://www.cellh5.org/">CellH5</a>”, 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 <span class="citation">Sommer et al. (2013)</span>.</p>
 <p>In the code below, we use the <a href="https://github.com/CellH5/cellh5-R">cellh5</a> R–package to import the data. The file <code>_all_positions.ch5</code> contains links to the other <code>ch5</code> files that contain the full data of the plate. We are only interested in the predictions produced by the machine learning algorithm, so we only extract this part of the file.</p>
 <pre class="sourceCode r"><code class="sourceCode r">path &lt;-<span class="st"> </span><span class="kw">file.path</span>(switch(.Platform$OS.type, <span class="dt">unix =</span> <span class="st">&quot;/g&quot;</span>, <span class="dt">windows =</span> <span class="st">&quot;Z:&quot;</span>),
                   <span class="st">&quot;embo2016/htm2016/P12_Visual_Exploration/hdf5/_all_positions.ch5&quot;</span>)
@@ -139,98 +138,25 @@ predictions &lt;-<span class="st"> </span><span class="kw">C5Predictions</span>(
 <div id="tabulate-the-raw-data" class="section level1">
 <h1><span class="header-section-number">5</span> Tabulate the raw data</h1>
 <p>We now tabulate the raw data: we compute how many cells are assigned to each class for each well. The result is a data matrix, which contains the wells of the screen plate in the columns and the counts for the respective classes in the rows.</p>
-<p>This is a typical example of a “wide” data table, where the variables contained in the data set spread across multiple columns.</p>
+<p>This is a typical example of a “wide” data table, where the variables contained in the data set spread across multiple columns (here we only show the first six ones).</p>
 <pre class="sourceCode r"><code class="sourceCode r">raw_data &lt;-<span class="st"> </span><span class="kw">sapply</span>(c5_pos,
                     function(pos){
                       predictions &lt;-<span class="st"> </span><span class="kw">C5Predictions</span>(c5f, pos, <span class="dt">mask =</span> <span class="st">&quot;primary__primary&quot;</span>, <span class="dt">as =</span> <span class="st">&quot;name&quot;</span>)
                       <span class="kw">table</span>(predictions)
                     })                     
 
-<span class="kw">head</span>(raw_data)</code></pre>
-<pre><code>             WA01_P1 WA02_P1 WA03_P1 WA04_P1 WA05_P1 WA06_P1 WA07_P1 WA08_P1
-   ana           332     914     657     551     237     483     482     198
-   apo          5435    3757    2413    1951     949    1945    1584    4569
-   artefact     1059    1288     333     357     645     233     159     227
-   binuclear    2604    1534     680     377    3514     224     464      53
-   inter       21865   45615   29005   24143   12293   17698   22938    3404
-   map          2910    2142     921     726     445     513     633    1344
-             WA09_P1 WA10_P1 WA11_P1 WA12_P1 WB01_P1 WB02_P1 WB03_P1 WB04_P1
-   ana           462     235     231     371     876      82     794     566
-   apo          2510    2464    4912    3130    2368    7196    3504    1698
-   artefact      587     266     651    1019     463     149     902     396
-   binuclear     321     227     224     119     624     126     907     584
-   inter       17779   12945    6258   12330   41367    5824   32848   25141
-   map           752     460     712     512    1233    1624    1442     825
-             WB05_P1 WB06_P1 WB07_P1 WB08_P1 WB09_P1 WB10_P1 WB11_P1 WB12_P1
-   ana           549     526     574     412     419     283     232     395
-   apo          1188    1066    1460    1352    1429    4461    2347    3621
-   artefact      357     191     576     541     192     352     212     706
-   binuclear     370     380     322     143    1331     210     220     272
-   inter       26142   27145   30655   19599   19791    3907   14211   11680
-   map           657     506     810     442     551     495     539     818
-             WC01_P1 WC02_P1 WC03_P1 WC04_P1 WC05_P1 WC06_P1 WC07_P1 WC08_P1
-   ana           597     691     422     465     651     588     314     522
-   apo          1571    1893    1232    1925    5161    1213     911    1808
-   artefact      572     763     393     374     277     418     233     200
-   binuclear     247     254     230     342     492     264     207     215
-   inter       33542   29535   21891   21153   20998   24261   15189   25146
-   map           696     730     413     580    1644     460     375     460
-             WC09_P1 WC10_P1 WC11_P1 WC12_P1 WD01_P1 WD02_P1 WD03_P1 WD04_P1
-   ana           307     305     347     500     582     660     289     611
-   apo          1549    1693    1187    6346     971    1844    3825    1248
-   artefact      261     405     335     716     354     521     306     602
-   binuclear     240     119     188     206     868     226      21     244
-   inter       11454   12725   13813    6049   28882   28703    3381   28199
-   map           566     358     409    1206     325     521    1200     404
-             WD05_P1 WD06_P1 WD07_P1 WD08_P1 WD09_P1 WD10_P1 WD11_P1 WD12_P1
-   ana           380     266     499     325     368     363     412     317
-   apo          1237    1980    1362    6111    1387    1187    2293    2205
-   artefact      203     362     295     217     185     164     165     340
-   binuclear     125    1784     294      18     190     207     812     207
-   inter       12969   16765   23615    1060   16208   17472   13893   10730
-   map           330    1767     382    1116     412     393     467     383
-             WE01_P1 WE02_P1 WE03_P1 WE04_P1 WE05_P1 WE06_P1 WE07_P1 WE08_P1
-   ana           745     680     507     154     514     411     217     402
-   apo          1183    2117     805    1724    1045     973    1184    1290
-   artefact      320     451     287     386     325     331     413     211
-   binuclear     406     340     208    3129     525     113    4043     157
-   inter       34054   30180   28076    7680   22809   22116   12057    9260
-   map           594     762     354    1701     457     362     511     622
-             WE09_P1 WE10_P1 WE11_P1 WE12_P1 WF01_P1 WF02_P1 WF03_P1 WF04_P1
-   ana           310     490     288     419     765     428     481     480
-   apo          1220    2642    1766    2462    1846    1039     917    1447
-   artefact      298     382     217     819     555     237     154     152
-   binuclear     233     690     252     283     432     377    1014     235
-   inter       13417   22400   14652   15133   33687   20029   20915   22107
-   map           466     648     575     456     643     285     478     471
-             WF05_P1 WF06_P1 WF07_P1 WF08_P1 WF09_P1 WF10_P1 WF11_P1 WF12_P1
-   ana           535     419     885     598     605     470     384     213
-   apo          1540    1187    4333    1574    1746    2634    3053    2160
-   artefact      362     193    2198     235     312     617     448     538
-   binuclear     285     168    3292     256     222     329     739     206
-   inter       28177   19900   31721   25656   26475   18373   13451   10599
-   map           819     401    3434     612     811     606    1260     241
-             WG01_P1 WG02_P1 WG03_P1 WG04_P1 WG05_P1 WG06_P1 WG07_P1 WG08_P1
-   ana           595     545     281     325     402     175     520     439
-   apo          1641     857     804    1020     944    4421    1741    1015
-   artefact      356     383     217     125     174     301     372     323
-   binuclear     359     265     150     252     200     136     259     270
-   inter       27414   27078   13163   16167   18310    8163   24188   20097
-   map           485     353     259     352     495    1590     564     379
-             WG09_P1 WG10_P1 WG11_P1 WG12_P1 WH01_P1 WH02_P1 WH03_P1 WH04_P1
-   ana           111     286     300     547     467     501     376     161
-   apo          4381    2936    2617    4427    3493    1415     659    4606
-   artefact      451     275     378     593     430     379     245     153
-   binuclear      72     203     316     273     265     215     143     196
-   inter        1950    8388   11799    9959   21343   21073   17503    3143
-   map          1770     353     455     607     465     444     188    2457
-             WH05_P1 WH06_P1 WH07_P1 WH08_P1 WH09_P1 WH10_P1 WH11_P1 WH12_P1
-   ana           464     470     378     541     397     407     366     457
-   apo          1470    1073     854     752    2079    3193    2891    5644
-   artefact      242     157     298     485     561     529     500     884
-   binuclear     185     192     317     365     234     105     319     697
-   inter       21745   18543   16349   27280   18633   11491   12876    8544
-   map           511     502     410     435     573     397     600     912</code></pre>
+raw_data[, <span class="dv">1</span>:<span class="dv">6</span>]</code></pre>
+<pre><code>             WA01_P1 WA02_P1 WA03_P1 WA04_P1 WA05_P1 WA06_P1
+   ana           332     914     657     551     237     483
+   apo          5435    3757    2413    1951     949    1945
+   artefact     1059    1288     333     357     645     233
+   binuclear    2604    1534     680     377    3514     224
+   inter       21865   45615   29005   24143   12293   17698
+   map          2910    2142     921     726     445     513
+   meta          238     495     334     363     166     280
+   polylobed    2726     712     368     226     553      46
+   prometa      2489    1319     989     656     293     756
+   telo         1480    4045    1860    1088     232     808</code></pre>
 </div>
 <div id="reshaping-the-screen-data-and-joining-the-plate-annotation" class="section level1">
 <h1><span class="header-section-number">6</span> Reshaping the screen data and joining the plate annotation</h1>
@@ -452,8 +378,8 @@ heatmap</code></pre>
 <p>Bar charts however, often represent the mean or a count statistic, while histograms are bar charts where the bars represent the binned count or density statistics and so on.</p>
 <p>Let’s start by creating a simple plot: Data from an enzyme-linked immunosorbent assay (ELISA) assay. The assay was used to quantify the activity of the enzyme deoxyribonuclease (DNase), which degrades DNA. The data are assembled in the R object <code>DNase</code>, which conveniently comes with base R. <code>DNase</code> is a dataframe whose columns are <code>Run</code>, the assay run; <code>conc</code>, the protein concentration that was used; and <code>density</code>, the measured optical density.</p>
 <pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(DNase, <span class="kw">aes</span>(<span class="dt">x =</span> conc, <span class="dt">y =</span> density, <span class="dt">color =</span> Run)) +<span class="st"> </span><span class="kw">geom_point</span>() </code></pre>
-<p><img 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" /></p>
-<p>We just wrote our first <code>sentence</code> using the grammar of graphics. Let us deconstruct this sentence. First, we specified the dataframe that contains the data, <code>DNase</code>. Then we told <code>ggplot</code> via the aesthetics <code>aes</code> argument which variables we want on the <span class="math">\(x\)</span>- and <span class="math">\(y\)</span>-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 <code>geom\_point</code>.</p>
+<p><img 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" /></p>
+<p>We just wrote our first <code>sentence</code> using the grammar of graphics. Let us deconstruct this sentence. First, we specified the dataframe that contains the data, <code>DNase</code>. Then we told <code>ggplot</code> via the aesthetics <code>aes</code> argument which variables we want on the <span class="math">\(x\)</span>- and <span class="math">\(y\)</span>-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 <code>geom_point</code>.</p>
 </div>
 <div id="principal-component-analysis-pca-to-for-data-visualization" class="section level2">
 <h2><span class="header-section-number">9.4</span> Principal component analysis (PCA) to for data visualization</h2>
@@ -496,13 +422,16 @@ V=2\times \mbox{ Beets }+ 1\times \mbox{Carrots } +\frac{1}{2} \mbox{ Gala}+ \fr
    [17] BiocStyle_2.0.3 
    
    loaded via a namespace (and not attached):
-    [1] Rcpp_0.12.5        formatR_1.4        RColorBrewer_1.1-2 plyr_1.8.4        
-    [5] tools_3.3.1        zlibbioc_1.18.0    digest_0.6.10      evaluate_0.10     
-    [9] gtable_0.2.0       DBI_0.5-1          ggrepel_0.6.3      yaml_2.1.13       
-   [13] parallel_3.3.1     grid_3.3.1         R6_2.2.0           foreign_0.8-67    
-   [17] magrittr_1.5       scales_0.4.0       htmltools_0.3.5    assertthat_0.1    
-   [21] mnormt_1.5-5       colorspace_1.2-7   labeling_0.3       stringi_1.1.2     
-   [25] lazyeval_0.2.0     munsell_0.4.3</code></pre>
+    [1] Rcpp_0.12.5          RColorBrewer_1.1-2   BiocInstaller_1.22.3
+    [4] formatR_1.4          plyr_1.8.4           tools_3.3.1         
+    [7] zlibbioc_1.18.0      digest_0.6.10        evaluate_0.9        
+   [10] gtable_0.2.0         DBI_0.5-1            ggrepel_0.6.3       
+   [13] yaml_2.1.13          parallel_3.3.1       grid_3.3.1          
+   [16] R6_2.2.0             foreign_0.8-67       magrittr_1.5        
+   [19] scales_0.4.0         codetools_0.2-15     htmltools_0.3.5     
+   [22] assertthat_0.1       mnormt_1.5-5         colorspace_1.2-7    
+   [25] labeling_0.3         stringi_1.1.1        lazyeval_0.2.0      
+   [28] munsell_0.4.3</code></pre>
 <div class="references">
 <h1>References</h1>
 <p>Neumann, Beate, Thomas Walter, Jean-Karim H<span>’e</span>ric <span>’e</span>, Jutta Bulkescher, Holger Erfle, Christian Conrad, Phill Rogers, et al. 2010. “Phenotypic Profiling of the Human Genome by Time-Lapse Microscopy Reveals Cell Division Genes.” <em>Nature</em> 464 (7289). Springer Nature: 721–27. doi:<a href="http://dx.doi.org/10.1038/nature08869">10.1038/nature08869</a>.</p>