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.
- 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?
- 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?
- Show the amount of differentially expressed genes using an MA Plot or a Volcano plot. Highlight genes of interest.
- 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
- Which method did you choose and why?
- What annotation data did you use and why? What version of the annotation are you using?
- How many genesets were returned with what thresholds?
- 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
- Do the over-representation results support conclusions or mechanism discussed in the original paper?
- 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
- Isserlin, R. (2022, ). BCB420 - Computational Systems Biology - Lecture - . Toronto; Quercus.