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- | **B)** Let's define the //impact// of a set of genes to be the p-value of a log-rank test that determines whether that gene set is associated with survival. Specifically, the null hypothesis of the log-rank test is that when all of these genes __are mutated together__, the survival does not change. I.e., we compare the KM plot corresponding to all cases who have mutations in all of these genes with the km plot of the rest of cases to compute a p-value. A significant p-value shows that these two survival curves differ.\\ | + | **B)** Let's define the //impact// of a set of genes to be the p-value of a log-rank test that determines whether that gene set is associated with survival. Specifically, the null hypothesis of the log-rank test is that when all of these genes __are mutated together__, the survival does not change. I.e., we compare the cases who have mutations in all of these genes with the the rest of cases to compute a p-value. A small p-value indicates a significant difference between the two survival curves (KM plots) corresponding to these two groups.\\ |

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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? | 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? |