as to improve transparency of results for publica on, we have developed mESCexplorer ( Fig 71 ), using the shiny web framework for R (Chang et al. 2015) .
5.1.3.1 General app layout
mESCexplorer consists of mul ple elements that allow the user to access summary sta s cs and visualiza ons of several parts of our analysis outlined in previous sec ons of this chapter. It consists of two main parts:
Data tabs (on the le ), displaying sta s cs and auxiliary informa on on the level of individual genes, gene sets, and pre-computed gene clusters (Interac on N2 and cons tu ve gene sets, as described earlier in this chapter).
Auxiliary tabs (on the right), showing visualiza on and addi onal informa on for items selected in data tabs (genes, gene sets, or clusters). Central to this sec on of the app is the knockout sample t-SNE map, which shows an interac ve t-SNE projec on of all knockout samples in rela on to the wild type. Currently, the projec on can be based on naive marker expression, N2B27 expression of RC9 -differen a on-associated genes (“N2 (diff.)”), expression of all genes in 2i, or expression of all genes in both N2B27 and 2i. The most important features of the knockout map are (a) the selec on of knockout samples or groups of samples to calculate aggregate sta s cs in the data tabs, and (b) the visualiza on of several knockout-wise sta s cs by colour, such as individual genes log-fold-changes, gene set combined p-values, or averaged z-values of clusters (as explained in the following subsec ons).
5.1.3.2 Gene level information
Differen al expression analysis results of individual genes are accessible in the “Genes” tab. Two types of informa on are available in the main table:
The first type depends on the ac ve selec on of knockout samples. If more than one knockout sample is selected, values averaged over the selec on will be shown. These condi onal sta s cs include p-values of knockout:RC9 interac on, KO N2 vs. RC9 N2 , and KO 2i vs. RC9 2i coefficients, as
well as the coefficients (log-fold-changes) themselves. Addi onally, z-values over the
distribu on of all knockouts are shown for KO N2 vs. RC9 N2 and KO 2i vs. RC9 2i log-fold-changes.
The second type are global sta s cs that are independent of the knockout sample selec on. These currently include only the naive marker R 2 value, i.e. the shared variance of the gene expression vector with the expression pa erns of naive pluripotency markers.
Addi onal informa on is shown below the main table upon selec on of a table row (gene). This includes a line chart of the gene’s expression level in 2i and N2B27, for the RC9 wild type as well as any selected knockout samples. Also, the user may inves gate the neighborhood of the selected gene in either its 2i or N2B27 co-expression network (see sec on 5.1.3.3). Further tabs to the right of the line chart display all GO annota ons and reactome pathways the gene is annotated with.
5.1.3.3 Geneset level information
Aggregated differen al expression results on the basis of gene sets are available in the “Genesets” tab. Generally, gene sets are genes grouped by a common annota on. The user begins by selec ng the “Gene set source”: N2 network modules, 2i network modules, GO terms, Reactome pathways, or others. Summary p-values of gene sets are calculated from one of three gene-level comparisons, to be selected in the “Comparison” drop-down menu: “Interac on” ( knockout:RC9 ), “N2, KO vs. WT” ( KO N2 vs. RC9 N2 ) , and “2i, KO vs. WT” ( KO 2i vs.
RC9 2i ). We made of the Fisher method (Fridley et al. 2010) to combine the p-values of individual genes into a gene set p-value. If more than one knockout sample is selected, the combined p-value in all selected samples is averaged.
Network modules are non-overlapping sets based on a co-expression network learned on N2B27 or 2i expression data using the GENIE3 package (Huynh-Thu et al. 2010) . GENIE3 is a random forest based algorithm suited to detect linear as well as non-linear associa ons (edges) between gene expression pa erns. We thresholded the edge weights calculated by GENIE3 at 10% of the weight of the strongest edge to obtain a discrete directed network. We then par oned this network into modules using the infomap algorithm as implemented in igraph (Rosvall & Bergstrom 2008) . Enriched biological func ons in each network module and their BH-corrected significances, i.e. GO terms and reactome pathways, were calculated using
5 genes) are displayed on the right-hand side of the app under “Network module annota ons”. The remaining choices of gene sets, GO terms, Reactome pathways, and Others, can be
mutually overlapping. Others contains curated sets of genes, such as naive pluripotency markers, or all genes contained in the “PluriNet” (Müller et al. 2008) .
5.1.3.4 Pre-computed clusters
Results of the cluster analysis presented in this thesis are accessible in the “Pre-computed clusters” tab. The clustering, encompassing the cons tu ve knockout response clusters and Interac on N2 clusters, can be selected by drop down menu (“Clustering”). This will ini alize a heatmap showing the mean of KO N2 vs. RC9 N2 log-fold-changes for each cluster and knockout,
along with the corresponding naive marker log-fold-changes (similar to Fig 62 ). A cluster is then selected for further inspec on. Cluster averaged log-fold-changes, or their corresponding z-values from row-normaliza on, are visualized on the knockout t-sne map. The results of a GO enrichment analysis of the cluster genes (using SETHRO) are displayed in the main table. Via the “go to cluster genes” bu on it is furthermore possible to switch to the “Genes” tab and inspect, sort, and filter all cluster genes.
5.1.3.5 Development and outlook
Current development of mESCexplorer focuses on two areas:
Firstly, providing comprehensive documenta on, as well as downloads of plain data files underlying the analysis, directly from within the app. This step is crucial to ensure the data and analysis are easily accessible as a resource for other researchers in the field.
Secondly, the streamlining of analysis tools. Currently, mul ple redundant ways of analysis are integrated in the app, reflec ng its use as a collabora ve tool that is frequently being adapted to new developments in the underlying analysis. The final objec ve is focus mESCexplorer on reproducing the workflow presented in the eventual publica on of this project.
In summary, mESCexplorer is a web app designed to provide transparent access to data
analysed in this project. It will be released as a freely available resource upon publica on of the project.
Fig 71 : the mESCexplorer web tool.
Screenshot from the R Shiny web tool developed for this project. Selected is cluster one of the InteractionN2 subset clustering.