Difference between revisions of "Module 4: How do "our expectations" confound the results of our analyses?"

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* Slides
 
* Slides
** Slides of the lesson of Tuesday Dec. 8th: [[Media:Slides_Tue.Dec8.pptx]]
+
** Slides of the lesson of Monday Dec. 5th:  
** Slides of the lesson of Monday Dec. 14th: [[Media:Slides_Dec14.pptx]]
+
** Slides of the lesson of Monday Dec. 12th:  
  
 
* Data and Code
 
* Data and Code
** Data and R code for practical exercise: [[Media:data_and_code_14.12.15.zip]]
+
** Data and R code for practical exercise

Revision as of 16:26, 2 December 2016

  • Title: "How do "our expectations" confound the results of our analyses?"
  • Paper to be examined: “Mutual Exclusivity analysis identifies oncogenic network modules”, Genome Research 2012 22, 398-406 [1]

+ cancer genomic studies from The Cancer Genome Atlas (TCGA).

  • Key claim of the paper: "We introduce here a simple but effective method to evaluate the statistical significance of correlations between genomic events, that concurrently preserves both tumor selectivity and tumor heterogeneity"
  • Data and Code
  • Schedule:
    • H1: General introduction to the cancer genomics concepts and motivation introduced in the paper
    • H2: The concept of random expectation (the null model)
    • H3: How should we model cancer heterogeneity to estimate the significance of mutual exclusivity?
    • H4-5: Use R to load and analyze the first cancer genomics dataset (compare alteration distributions and permutation models)
    • H6: Reproduce mutual exclusivity analyses proposed in the original paper.
    • H7-8: Examine TCGA cancer genomic studies to perform mutual exclusivity analyses under different permutation models
    • H9: Summarize and present the results.
  • Key bioinformatics concept of this module:
    • The importance of the null model (your expectations)
    • Properly assessed Mutual Exclusivity inform on biological pathways altered in cancer
  • Slides
    • Slides of the lesson of Monday Dec. 5th:
    • Slides of the lesson of Monday Dec. 12th:
  • Data and Code
    • Data and R code for practical exercise