5. Assignment 2: Differential Gene expression and Preliminary ORA - bcb420-2022/RuoXuan_Wang GitHub Wiki

Objective

Perform thresholded over-representation analysis of genes ranked according to differential expression to identify themes.

Duration

Time estimated: 15h; taken h;
date started: 2022-03-11; date completed: 2022-03-

Progress

Tasks:

1. Differential Gene Expression

Conduct differential expression analysis with your normalized expression set from Assignment #1. Define your model design to be used to calculate differential expression - revisit your MDS plot from Assignment #1 to demonstrate your choice of factors in your model.

  1. Calculate p-values for each of the genes in your expression set. How many genes were significantly differentially expressed? What thresholds did you use and why?
  2. Multiple hypothesis testing - correct your p-values using a multiple hypothesis correction method. Which method did you use? And Why? How many genes passed correction?
  3. Show the amount of differentially expressed genes using an MA Plot or a Volcano plot. Highlight genes of interest.
  4. Visualize your top hits using a heatmap. Do you conditions cluster together? Explain why or why not. Make sure all your figures have proper heading and labels. Every figure included in the report should have a detailed figure legend

2. Thresholded over-representation analysis

With your significantly up-regulated and down-regulated set of genes run a thresholded gene set enrichment analysis

  1. Which method did you choose and why?
  2. What annotation data did you use and why? What version of the annotation are you using?
  3. How many genesets were returned with what thresholds?
  4. Run the analysis using the up-regulated set of genes, and the down-regulated set of genes separately. How do these results compare to using the whole list (i.e all differentially expressed genes together vs. the up-regulated and down regulated differentially expressed genes separately)? 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.

3. Interpretation

relate results back to initial data and question

  1. Do the over-representation results support conclusions or mechanism discussed in the original paper?
  2. Can you find evidence, i.e. publications, to support some of the results that you see. How does this evidence support your results.

Conclusion and outlook

References

  1. Isserlin, R. (2022, ). BCB420 - Computational Systems Biology - Lecture - . Toronto; Quercus.