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r_test [2019/06/06 03:20] – [Question 2:] adminr_test [2020/01/23 15:09] – [Question 2:] admin
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   des <- ????   des <- ????
   Data <- log(exprs(gset[[1|]])+0.001)   Data <- log(exprs(gset[[1|]])+0.001)
-  fit <- ImFit(Data, des) ## !+  fit <- lmFit(Data, des) ## !
   ...   ...
 </code> </code>
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 ---- ----
 +
  
 ==== Question 2: ==== ==== Question 2: ====
  
-Using the [[https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html|maftools]] and [[http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html|TCGAbiolinks]] packages, determine the 3 most frequently mutated genes in liver cancer. Which of these 3 mutations is more predictive of survival? To answer this question, write a function that takes as input a gene name, and save KM plots in png format. Add the p-value as a legend in the plot. Deliverables are similar to question 1.\\ +**A)** Using the [[https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html|maftools]] and [[http://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html|TCGAbiolinks]] packages, determine the 3 most frequently mutated genes in liver cancer. Which of these 3 mutations is more predictive of survival? To answer this question, write a function that takes as input a gene name, and save KM plots in png format. Add the p-value as a legend in the plot. Deliverables are similar to question 1. 
-__Bonus__: Let's define the //impact// of a set of genes to be the p-value of a log-rank test with the null hypothesis that when all of these genes are mutated together, the survival does not change. Write a function ''most.impact()'' that takes as input two ''k1'' and ''n1'' integers, and in the list of ''n1'' most mutated genes, finds the names of the ''k1'' genes with the best impact. Your function should return the names of the best ''k1'' genes, and also their impact. Run your function for ''k1=3'', and ''n1=3'', ''10'', and ''100''. What the biological interpretation of your results?+ 
 +\\ 
 +**B)** Let's define the //impact// of a set of genes to be the p-value of a log-rank test with the null hypothesis that when all of these genes are mutated together, the survival does not change. Write a function ''most.impact()'' that takes as input two ''k1'' and ''n1'' integers, and in the list of ''n1'' most mutated genes, finds the names of the ''k1'' genes with the best impact. Your function should return the names of the best ''k1'' genes (i.e., the set of genes with the best log-rank p-value), and also their impact. Run your function for ''k1=3'', and ''n1=3'', ''10'', and ''100''. What is the biological interpretation of your results? 
 + 
 +__Hint:__ Use the ''utils::c?m?n()'' function, where you need to guess the question marks. 
 + 
 +**Bonus**: Implement the ''utils::c?m?n()'' function yourself using dynamic programming. Compare the running time of your implementation vs. the utils implementations using large inputs that require at least a couple of minutes.