BIOL398-03/S13:Class Journal Week 12
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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?
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)
