# BIOL398-01/S11:Class Journal Week 12

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## Contents |

## Instructions

### Formatting

- Link to your journal entry from your user page.
- Link back from the journal entry to your user page.
- Sign your portion of the journal with the standard wiki signature shortcut (
`~~~~`

). - Add the "BIOL398-01/S11" category to the end of the wiki page (if someone has not already done so).

### Reflection

- What aspect of this assignment came most easily to you?
- What aspect of this assignment was the most challenging for you?
- What (yet) do you not understand?
- For this week's and last week's assignment we computed (or the software did) three different p values: per individual gene, per profile, and per Gene Ontology term. State in your own words what we need each of these p values for and what are they telling us?

## Class Responses

### Sarah Carratt's Journal Entry

- Following the directions made this assignment so easy to understand/complete. I was able to finish all the work in class so that I just had to type up my notes and look up the definitions tonight. Thank you, thank you for making this easy for me!
- While it wasn't particularly challenging, the longest part of the assignment was the application of the definitions and finding the willpower to look up each GO term and then figure out why it was significantly affected.
- I'm nervous for the model, and the diagram doesn't fully make sense yet.
- The P value per individual gene is the measure of significance each gene had for the time points. The P value per profile tells us the accuracy of each profile, and if the number of genes shown is simply expected by chance or a result of something else. Lastly, the P value per Gene Ontology term allows us to examine categories of genes according to functions or accepted clusters. From these p-values, we can tell if the data clusters can be relied upon as significant.

Sarah Carratt 02:20, 12 April 2011 (EDT)

### Carmen E. Castaneda's Journal Entry

- The part of the assignment that came easily to me was manipulating the data whether it was in excel or the other programs.
- The most challenging part of the assignment was trying to understand the different genes and how the cold shock affected them.
- I don't understand the last part of the assignment because I was unable to get the matrix. I also don't understand what exactly we did with the transcriptions genes on the YEASTRACT website, what I mean is I don't understand the information we received from that website.
- The p value for the per individual gene told us the expected "big" effect that gene had at the 5% through .01% of the time. The per profile p value tells us the effect of that set of genes on the cell at 5% of the time. While the per Gene Ontology term p value tells us the percentage a given category is having on the cell.

--Carmen E. Castaneda 02:15, 12 April 2011 (EDT)

### James C. Clements' Journal Entry

- The part of the assignment that came most easily to me was figuring out what to do. The directions clearly stated exactly what to do for each step of the process. As nice as this was for me (being a student who is pressed for time because of his thesis and looming graduation), I'm not quite sure if this "cookbook recipe" format for assignments is the best way to go for upper-division coursework.
- I have had difficulties with YEAST tract generating the list of genes grouped by transcription factor. The site persistently has given me an error message stating that it could not open a file.
- I still don't understand what we're going to model.
- Each p value gives us an idea of how significant the set of genes (or particular genes are). If we expect 5% of genes to perform in a certain way and we obtain data that states that 5% of our genes do just that, we've found nothing. If, on the other hand, our statistics determine that something should only happen 5% of the time and our experiments are able to induce it nearly 100% of the time, then it will be proven that that specific gene/cluster/profile has a significant role in the process.

James C. Clements 01:17, 12 April 2011 (EDT)

### Nicholas A. Rohacz's Journal Entry

- The part of this assignment that came most easily to me was going through all the GO processes and making the graphs, not only because it was easy but because I enjoyed seeing how the genes were exactly regulated throughout the time points.
- The final step was most challenging because I could not get it to generate a jpeg even though the instructions matched exactly what was stated. I will check in class what was wrong.
- I still do not understand exactly what we are going to do with all of this data. I have a general idea but I would like to figure out the whole process, doing steps just breaks up the process for me and makes it harder to understand.
- The p values let us determine exaclty what was significant in each process. This lets us compare what was found to be significant to what we were predicting to be significant. If we find that every gene we thought was significant is significant in each p-value, then we can start to understand the process. However if we find a large number of genes that are significant that were not predicted to be significant, then we know we are either missing something or part of the experiment went wrong somewhere, hence the sanity test.

Nicholas A. Rohacz 01:30, 12 April 2011 (EDT)

### Alondra Vega's Journal Entry

- The most easy thing about the assignment was working with STEM.
- The most challenging was choosing the transcription factor and figuring out why it is important to cold shock. Also, I was never able to get the matrix to work.
- I feel that I do not understand the p values and why we need all the different ones. Also, I am having trouble seeing the "big picture" for the project.
- I will give this a try. The individual p values show how significant the expression of the gene was at a certain time point. The p-value per profile shows how significant the the expression of the cluster. Since the cluster are categorized by pathway or regulation of transcription factor, then it might show how significant the transcription factors are. the p value for the Go terms show the significance of the expression of the genes that are associated in that particular category. We have the flexibility to say how large or small we want the p value to be, so it could be significant.

Alondra Vega 23:28, 11 April 2011 (EDT)