April 03 2013

To-Do-List:
1. Run the program with robust limma.
2. Run the program with different adjusting methods

Details:
1.   Run the program with robust limma.
The program was already being run with robust limma so I tried running it with normal non-robust limma. The result was much more limited compared to the robust limma.

2. Run the program with different adjusting methods
There are several adjusting methods that can be used in Limma to adjust the p- value, given at : http://rss.acs.unt.edu/Rdoc/library/stats/html/p.adjust.html
The methods consist of:
1. holm
2. hochberg
3. hommel
4. bonferroni
5. BH
6. BY
7. fdr
8. none

With the adjusting method set to bonferroni, I noticed that all the adjusted p-values are now 1 which sounded very unusual to me and so I started debugging the code line by line to see where things are not going in the right way.
while running the program with bonferroni method I realized that the expression Matrix didnt match the design specified. In our design vector we have 10 zeros at the top and 24 1s at the bottom, but we marked our ARs by 1 and NRs by 0 and the number of ARs was 10 and NRs 24.
In addition, although we are sorting the expression matrix in a way that the ARs are first and the NRs re second, this doesnt actually happen.
So my next task would be to figure out why the expression matrix is not sorted.