A2: Differential Gene Expression Analysis and Preliminary ORA

Objective

F
*

Started: 2023-03-16 | Completed: 2023-03-20
Estimated time: 12 hrs | Actual time: 15 hrs

Procedure

• Conduct differential gene expression analysis with the package edgeR.
  • First, I needed to create a design model for my data matrix. To do this, I needed to define my groups and evaluate the dispersion of my data. As we are using edgeR, which requires data to follow a negative binomial distribution, I needed to check if my data does the same. As for my groups, we need to consider what biological question we are asking. My data has two major factors: tissue and patient group. Therefore, I needed to include these two groups into my model.
  • When I was making my comparisons, I somehow got the same number of DE genes between the two patient groups for each tissue. I realized that I had a typo in my code. Lesson learnt: really read through your code, ESPECIALLY if you are copying the same line for a different variable.
• Thresholded Overrepresentation Analysis using g:Profiler
  • Used 3 sources: GO Biological Process (GO:BP), Reactome (REAC), and WikiPathways (WP).
  • These sources were used as we have already gone through them in H2: g:Profiler.

Results

Conclusion

References

Isserlin, R. (2021) Lecture 6 - Differential Expression.
Isserlin, R. (2021) Lecture 7 - Annotation Dataset and Intro to Pathway analysis.
Xie, Y., Dervieux, C., Riederer, E. (2022) R Markdown Cookbook https://bookdown.org/yihui/rmarkdown-cookbook/