Article : Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring By T. R. Golub and D. K. Slonim.
BIFS614 Data Structures and Algorithms
Assignment
K-Means Clustering in R (using R-Studio)
To complete this homework you will first have to download R and R-Studio.
Once you have installed R and R-Studio, make sure that you have the corresponding R script (BIFS614 Homework 4.R) on your desktop. Right click on it and open it in RStudio. The interface will open and should look like this:
If you are not familiar with R, the easiest way to execute the script will be one line at a time. Place your cursor at the end of the first line of the script and press the "RUN" button at the top of the upper left window. This will execute ONE LINE at a time. The bottom left window (the Console) will display the output results of the execution, while the upper right window will display any data objects created. The bottom right window will display any visuals (graphs/etc) created.
Step through the script one line at a time by pressing RUN. When you get to the line that loads the Bioconductor source, you should wait and be sure that the package is fully loaded before pressing RUN again. The next line, for biocLite, will also take some time to complete - just be patient.
You should also pay attention to the console output because it may ask you to update a package - if you are asked to update, you should go ahead and say yes. You'll have to place your cursor in the console window and type the letter it wants, which is a "y" for yes (but without the quotes) or an "a" for all if more than one package needs to be updated.
Be sure to read the script - there are many comments and instructions in there as well.
QUESTIONS TO ANSWER:
1. When you performed hierarchical clustering with the defaults set to 8, what did you see? What does this mean? Include an image as well as a description.
2. Modify the script to perform the hierarchical clustering for a different number of clusters (your choice but values between 5 and 12 are probably the most useful). Include an image of the new clustering - compare it to the original settings. What does this tell you?
3. The Golub et. al. (1999) paper describes this dataset. How does your clustering compare to the results that they found?