Functional Analysis - Sona Popat

Progress Summary - Functional Analysis - Sona Popat

Presentation:

Link: https://drive.google.com/file/d/1pkQsAamwrbkmjcaPVU3e0W-Y5jJ6G2oL/view?usp=sharing

Contribution: Wrote the code to generate the plots, created and delivered the presentation

Challenges Faced:

  • 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:

No filter:

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

Further Notes:

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.