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NYU_Transcriptomics

Final Project

PRESENTATION:

Each group will have 15 minutes: 10 minutes for presentation 5 minutes for questions

Please practice and time yourself, if your presentation is more than 12 minutes you will be penalized. As a guideline you should spend 1.5 minutes on a slide so that’s 8 slides (not including the title slide). Try to put a figure on every slide.

An example of what these slides can be:

  1. Title
  2. Biological significance of the study (why is it important)
  3. Summary of the dataset (what platform, number of chips, number of replicates, etc)
  4. Normalization of dataset – method and results
  5. Determination of Differentially expressed Genes – method and results
  6. Clustering of genes differentially expressed – method and results
  7. GO-term enrichment of differentially expressed genes and/or clusters
  8. Significance of your results.
  9. Summary / Conclusion

  REPORT:

My Amazing Project

Abstract: Quick summary of what you are studying, why and what you found and why this is interesting.

Introduction: A detail description about the sample/disease that you are studying. What are you hoping to find out from your analysis and why is this question important to biology? How are you going to try to answer this question? Use the paper in which the data was published. Don’t plagiarize, re-word the information and give reference to it.

Results: Write an explanation for methods that you used in each analysis analysis. Don’t paste your R-code here, make reference to the appendix.

Normalization: Method Results Figure Identify Genes differentially expressed: Method – why? Results Figure Cluster Differentially expressed genes: Method- why? Results Figure GO-term analysis of differentially expressed genes and or clusters: Method Results Figure

Discussion: From this analysis I learned that … Compared to the paper that published this dataset I got similar/different results because … It makes sense that there are ### differentially expressed genes in this dataset. The cluster that is of most interest is ## because its expression profile fits what I was looking for … The GO-terms for the differentially expressed genes and the clusters make perfect sense because …

References : There should be atleast one reference here (to the dataset that you are using).

Appendix: Provide you R code here separated by the 4 different sections discussed above. DON’T FORGET TO COMMENT YOUR CODE!

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