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| r_test [2021/05/16 15:13] – [Question 2:] admin | r_test [2021/05/26 00:52] – [Question 3] admin |
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| **An R test** \\ | **An R test** \\ |
| These simple questions are designed to test the applicants on their expertise in R, self learning, working under pressure, presenting their results, and explaining their work. | These simple questions are designed to test the applicants on their expertise in R, self learning, working under pressure, presenting their results, and explaining their work. If you get help from people and internet, please include the person's name and the link to the webpage in your answers. |
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| ==== Question 1 ==== | ==== Question 1 ==== |
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| | Write a recursive function that computes n!.\\ |
| | **Deliverables**: The source code of the function. |
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| | ==== Question 2 ==== |
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| - Install [[https://bioconductor.org/packages/release/bioc/html/limma.html|limma]] package from Bioconductor. | - Install [[https://bioconductor.org/packages/release/bioc/html/limma.html|limma]] package from Bioconductor. |
| - Use pheatmap function to plot the expression of the top 5 DE genes. | - Use pheatmap function to plot the expression of the top 5 DE genes. |
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| \\ - **Deliverables**: Report the number of DE genes, de.csv file, your script, heatmap.png, and the approximate number of hours it took you to do the test. Additionally, write a short description of what you did in 5-10 sentences. The description should indicate your proficiency in English writing. To test your ability to communicate with a biologist __who has little or no background in programming__, write the summary of your results in a paragraph titled "conclusion". \\ - You need to be able to orally explain all parts of your script including the above lines. Be prepared to explain the input, output, and process done by each function you use. \\ - If you are not familiar with gene expression and have little idea what limma does, try to learn the needed concept from the web, e.g., [[https://en.wikipedia.org/wiki/Gene_expression|Wikipedia]]. You can use information from tutorials, books, papers, experts in the fields, your friends, etc., however, make sure you can explain your work thoroughly. \\ | \\ **Deliverables**: Report the number of DE genes, de.csv file, your script, heatmap.png, and the approximate number of hours it took you to do the test. Additionally, write a short description of what you did in 5-10 sentences. The description should indicate your proficiency in English writing. To test your ability to communicate with a biologist __who has little or no background in programming__, write the summary of your results in a paragraph titled "conclusion". \\ - You need to be able to orally explain all parts of your script including the above lines. Be prepared to explain the input, output, and process done by each function you use. \\ - If you are not familiar with gene expression and have little idea what limma does, try to learn the needed concept from the web, e.g., [[https://en.wikipedia.org/wiki/Gene_expression|Wikipedia]]. You can use information from tutorials, books, papers, experts in the fields, your friends, etc., however, make sure you can explain your work thoroughly. \\ |
| - Follow the suggested file formats on the members' [[https://oncinfo.org/for_members|page]]. | - Follow the suggested file formats on the members' [[https://oncinfo.org/for_members|page]]. |
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| | ==== Question 3 ==== |
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| ==== Question 3: ==== | **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 2. |
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| **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. | |
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