1 Cytoscape time

I'll use the combined metric - seems more comprehensive since it combines both of the other two metrics.

What expression file do they mean in this tutorial? Counts or differential expression data? I assume the first? I'll try that.

I gave up on using cytoscape in R. Downloaded it for mac but its glitchyyy.Cytoscape keeps opening small windows with nothing on them. Especially hierarchical clustering windows opening whenever. Also, when I try to quit, a BLANK window prompting me to save comes up - but there's nothing on it! - Maybe its because I have so many nodes...

I restarted my computer - seems to be working now.

I think i've figured it out. I'm annotating with AutoAnnotation, making clusters, I want few clusters, by name (not description or gene, gene gets confusing)

Ok i think I have what i need now. TPO signaling is definitely biggest, and although that wasnt highlighted in A2 (and i dont think highlighted in the paper) there is definitely a link between DS and up-regulated TPO signaling!

2 Answering questions

1. Create an enrichment map - how many nodes and how many edges in the resulting map? What thresholds were used to create this map? Make sure to record all thresholds. Include a screenshot of your network prior to manual layout.

Annotate your network - what parameters did you use to annotate the network. If you are using the default parameters make sure to list them as well.

• 2637 nodes
• 10828 edges
• Q-value <= 0.05 (standard statistical significance)
• Edge cutoff (similarity) of 0.375 (default, not sure why)
• Annotated with amount of clusters 1/3 of the way between fewer/smaller and more/larger, and unchecked sigleton clusters, unchecked minimize cluster overlap, use WordCloud with most frequent words in cluster and adjacent words, label column Name, max words per label = 3, minimum word occurance = 1, adjacent word bonus = 8 (those 3 are defaults in advanced settings).
• I didnt add any signature gene sets because there were so many genes already that they would either be impossible to see, or there would be so many, the edges would block out all of the real data (i tried)

2. Make a publication ready figure - include this figure with proper legends in your notebook.

• Got it.

3. Collapse your network to a theme network. What are the major themes present in this analysis? Do they fit with the model? Are there any novel pathways or themes?

• What? Does she mean the clusters from autoannotate? but she says to annotate above. idk how to collapse.

4. Present your results with the use of tables and screenshots. All figures should have appropriate figure legends.

If using figures create a figures directory in your repo and make sure all references to the figures are relative in your Rmarkdown notebook.

• I will show my unannotated map, tables of nodes and clusters, and my annotated figure with legend.