Progress Summary - Functional Analysis - Sona Popat
Contribution: Wrote the code to generate the plots, created and delivered the presentation
- When I reached the cnetplot() step, the resulting plot was very messy and difficult to interpret because it was a web of thousands of genes - so the visualisation was not at all useful! I realised this error was arising because the initial DEGs list was too large, so I filtered this an increasing amount based on fold change until the visualisation of the cnetplot was improved. Before and after plots shown below:
Filter by fold change of ±2:
- I spent a lot more time focusing on interpreting the outputs of the analysis instead of creating them this week. For the most part, the visualisations helped with interpretations a lot, but I wasn’t sure how to interpret the survival plots. I researched this independently but could not find any specific resources on interpreting these in the context of cancer or other diseases, so I used the troubleshooting channel to gain advice on this. This really helped when creating and delivering the presentation at the end of the week!
- Last minute changes of plans meant I had to step in to create and deliver the deliverables presentation at the team meeting, only finding out that I would be doing it about an hour before the presentation was due to be given. However, this turned into one of my achievement highlights as I was really proud of how it turned out!
Summary of Work:
Gene Ontology Analysis - using enrichGO(), setReadable(), and barplot()
KEGG Analysis - using enrichKEGG() and dotplot()
Gene-Concept Network - using enrichDGN(), setReadable(), and cnetplot()
STRING-DB to identify sub-networks with functional links and hub genes that represented a flow of information, for example in a signalling pathway
Transcriptional Factor Analysis - downloading data from MSigDB - GSEA, using cnetplot()
Survival Analysis - using GEPIA to produce survival plots, beginning to interpret survival plots
Focussing on the biological implications of the results of the functional analysis
Frequently communicated with my team on Slack to help troubleshoot and give each other guidance, as well as provide support and encouragement
Created a presentation on colorectal cancer functional analysis and delivered this at the team meeting
This was my favourite step of the pipeline, especially coming from more of a biology and pathology background! I enjoyed seeing how the code and analysis from the weeks before (which alone didn’t seem all that meaningful) came together to tell a story about the disease. I also found it interesting to think about how this information could be used, for example to identify potential drug targets or understand potential risk factors of the disease.