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The attached dataset contains gene expression data for 200 genes for 100 indi-viduals. The first row of the dataset is the phenotype for each individual. Each consecutive set of 10 genes compose a pathway (eg: genes 21 ~ 30 are a pathway). You should code your own function to compute the following tests based on the mathematical basis and algorithm discussed in class. Basic packages are allowed to help with your computation. You could use existing function calls in sklearn, scipy, etc. to check the results of your own function.
Question 1. Estimate the mutual information between each transcript individually and the class label. Print the indices of the 10 transcripts with the highest mutual information with respect to the class label.
Question 2. Use a permutation test to compute top genes that are highly correlated with class label. Permute the class label for 1000 times. Please use Bonferroni correction and BH correction respectively with significance level α = 0.05 and q = 0.1. Please print the output of the identified significant genes for these two corrections respectively. How do they compare?
Question 3. Perform enrichment analysis using a hyper-geometric test on your set of 25 genes and each of the 20 pathways. Determine the 3 pathways for which the set is most enriched. Return the index of that pathway(a number between 1 and 20), as well as the p-value for the enrichment analysis.
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