BIOL398-03/S13:Class Journal Week 12
From OpenWetWare
Reflection
- After last weeks excel assignment, the excel work came easiest to me this time.
- Most challenging was making the connection between genes and why they were regulated differently during cold shock.
- I do not fully understand the connections between the cluster I had and the cold shock, further testing may help me out a bit when we make the model.
- Per gene p values told us if an individual gene was regulated at a statistically significant level during cold shock in the yeast, and the same goes for the clusters and the GO terms. If the p value is less than .05, then we had a statistically significant piece of data to work with, or something worth looking into.
Kevin Matthew McKay 20:15, 9 April 2013 (EDT)
Matthew E. Jurek Week 12
- What aspect of this assignment came most easily to you?
- Since we were working with two computer programs I've never used before, I'd say manipulating the Excel spreadsheets was the easiest. Before inputing data into STEM and the database, the excel spreadsheets had to be modified. We've worked with Excel multiple times before, and these modifications consisted of simple tasks like deleted rows or copying and pasting.
- What aspect of this assignment was the most challenging for you?
- Working with STEM was the most challenging step. Our data started in Excel but had to be modified before being input into Excel. The command window itself had a lot of different options. Luckily the instructions were very easy to follow. After running STEM, a screenshot was saved to powerpoint, then the GO term and gene lists had to be saved as different files. All of this had to be uploaded to lionshare with special permission for Dr. Dahlquist
- What (yet) do you not understand?
- Looking at the "network" I've uploaded, I'm trying to figure out how to tell what's being up-regulated or down-regulated based on the simple lines that connect all the genes.
- For the week 9 and 12 assignments 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?
- The p values for individual genes illustrate regulation changes for a certain gene. Unfortunately, this value can be misleading due to random chance as opposed to response to cold. As a result, a p value was calculated for entire profiles to better understand how regulation is impacted by cold shock, itself. Lastly, Gene Ontology incorporates transcription factors so this p value shows how transcription factos regulate genes or groups of genes.
- Matthew E. Jurek 14:12, 11 April 2013 (EDT):
Anthony J. Wavrin
- What aspect of this assignment came most easily to you?
- The aspect that came most easily was the actual manipulating of the excel files, such as filtering and the command to format the Matrix. This is because I have had more practice now and I am more comfortable dealing with larger excel files.
- What aspect of this assignment was the most challenging for you?
- The hardest aspect was most likely trying to draw all of the information together and trying to make a coherent conclusion from it all. There is a lot of data that has been analyzed and some of it may be due to chance, cold shock, or other outside factors. But, using the GO terms, it was a little easier to see the trends.
- What (yet) do you not understand?
- I am still completely confused about how we are supposed to use the large list of transcription factors to make a model that will sufficiently model the system that is already very variable between different studies.
- For the week 9 and 12 assignments 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?
- The individual genes are telling us very specifically which genes are being regulated which may give some indication but, due to the large amount of genes in a genome, it is hard to screen which ones are due to change and which are due to the actual cold shock. The p values of the profile give a significance of genes that are grouped together. This helps “screen” out some of the random chance genes due to processes being up regulated must have all of the individual aspects up regulated. Lastly, the Gene Ontology do a similar thing in which it matches the genes to transcription factors and measures if they would be up regulated. This can hint to what genes might be regulated together by the same transcription factors.
Anthony J. Wavrin 00:39, 11 April 2013 (EDT)
Laura Terada
- What aspect of this assignment came most easily to you?
- Changing the Excel to fit the STEM software and running STEM came the easiest to me. It wasn't hard to follow the directions for this section.
- What aspect of this assignment was the most challenging for you?
- Understanding the results from the STEM software was a little challenging; however, I was able to grasp what it means after further explanation in class. Another challenge was understanding the YEASTRACT section where we had to create another Excel and transpose the data.
- What (yet) do you not understand?
- I'm still not completely confident that I understand the STEM software and how it organizes all the data.
- For the week 9 and 12 assignments 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?
- The p value for individual genes tells us whether or not the gene itself is significantly changed in terms of expression. This p value would be difficult to analyze because gene expression can change due to other factors besides cold shock. Secondly, the p value per profile shows the significance of all the genes in a cluster/profile. Clusters group genes together to provide a better organization of gene expression changes due to cold shock. Lastly, the p value per Gene Ontology term includes transcription factors and groups these transcription factors with its respective genes.
Laura Terada 15:01, 11 April 2013 (EDT)
Ashley Rhoades Week 12
- What aspect of this assignment came most easily to you?
- The YEASTRACT part of the assignment came most easily to me because by then I had a fuller understanding of what we were trying to accomplish and YEASTRACT does quite a bit of work for you.
- What aspect of this assignment was the most challenging for you?
- There were problems putting my average log fold change data into the STEM program. I am probably going to have to go through my data and make sure that there's not wrong with it because I ended up using Kasey's data which did work in the software.
- What (yet) do you not understand?
- I generally understand what we did as far as analyzing the data goes but there were many components to this assignment. In other words, I feel I would benefit form reviewing the procedures and reminding myself what each step did and why.
- For the week 9 and 12 assignments 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?
- The p value for individual genes tells us that there was a significant difference between that particular gene's expression from the normal to the cold shock cell with a 5% significance threshold. Looking at the p values for profiles tells us what groups of genes changed together within the p<0.05 significance level. The Gene Oncology p value looks at transcription factors as well as genes, which allows us to see how genes may be up regulated or down regulated by the same transcription factors.
Ashley Rhoades 19:48, 11 April 2013 (EDT)
Salman Ahmad
- What aspect of this assignment came most easily to you?
- Most of this assignment was pretty easy. Taking screen shots and doing all of the copying and pasting were the easiest parts of this assignment.
- What aspect of this assignment was the most challenging for you?
- The hardest part of this assignment was following all of the directions that were posted. The directions were clear but there were a lot of them so it required a lot of going back and forth to make sure I did everything.
- What (yet) do you not understand?
- I don't really understand the details of the STEM program data. I'm not sure how exactly the graphs are organized and why some came in front of others (especially when later ones had higher p-values and more genes).
- For the week 9 and 12 assignments 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?
- The p-value for the indvidual gene is important because we want to know if the individual gene itself is significant. The p-value for profiles is important because there are many genes in each profile. The p-value lets us know how significantly similar all the genes in a given profile are. The p-value for the Gene Ontology term is probably related to how confident we are that a specific gene has a specific function, or that a specific profile of genes has the specified function. We can't actually see the processes occuring so many of the definitions must come from statistical evidence.
Salman Ahmad 20:51, 11 April 2013 (EDT)
