This journal entry is due on Wednesday, December 3 at midnight PST (Tuesday night/Wednesday morning). NOTE that the server records the time as Eastern Standard Time (EST). Therefore, midnight will register as 03:00.
- Brown, P.O. & Botstein, D. (1999) Exploring the new world of the genome with DNA microarrays Nature Genetics 21: 33-37.
- Campbell, A.M. and Heyer, L.J. (2003), “Chapter 4: Basic Research with DNA Microarrays”, in Discovering Genomics, Proteomics, and Bioinformatics, Cold Spring Harbor Laboratory Press, pp. 107-124. (Available on MyLMUConnect)
- Dahlquist, K.D., Salomonis, N., Vranizan, K., Lawlor, S.C., & Conklin, B.R. (2002) GenMAPP, A New Tool for Viewing and Analyzing Microarray Data on Biological Pathways. Nature Genetics 31: 19-20.
- DeRisi, J.L., Iyer, V.R., and Brown, P.O. (1997) Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science 278: 680-686.
- Doniger et al. (2003)
- Salomonis et al. (2007)
Overview of DNA Microarray Analysis
This is a list of steps required to analyze DNA microarray data.
- Quantitate the fluorescence signal in each spot in the microarray image.
- Typically performed by the scanner software, although third party software packages do exist.
- The image of the microarray slide and this quantitation are considered the "raw-est" form of the data.
- Ideally, this type of raw data would be made publicly available upon publication.
- In practice, the image data is usually not made available because the raw image file of one slide could be up to 100 MB in size.
- Also, some journals do not require data deposition as a requirement for publication, so often published data are not actually available anywhere for download.
- Microarray data is not centrally located on the web. Some major sources are:
- Calculate the ratio of red/green fluorescence
- Log(base 2) transform the ratios
- Normalize the log ratios on each microarray slide
- Normalize the log ratios for a set of slides in an experiment
- Perform statistical analysis on the log ratios
- Compare individual genes with known data
- Look for patterns (expression profiles; clusters) in the data (many programs are available to do this)
- Perform Gene Ontology term enrichment analysis (we will use MAPPFinder for this)
- Map onto biological pathways (we will use GenMAPP for this)
Individual Journal Assignment
- Store this journal entry as "username Week 13" (i.e., this is the text to place between the square brackets when you link to this page).
- Create the following set of links. These links should all be in your personal template; then use the template on your journal entry.
- Link to your journal entry from your user page.
- Link back from your journal entry to your user page.
- Link to this assignment from your journal entry.
- Don't forget to add the "BIOL368/F14" category to the end of your wiki page.
Continue Microarray Data Analysis
- Based on what you submitted for the Week 12 Assignment, the instructor will give you a customized protocol to follow for analyzing your microarray data.
- In general, you will be following the steps listed above up to step 7 for the Overview of DNA Microarray Analysis.
- You will keep a detailed electronic notebook page that details all of the data manipulations and calculations that you perform on your dataset.
- You will upload the Excel spreadsheet either to the OpenWetWare wiki or to Lionshare that contains the calculations you performed this week.
Shared Journal Assignment
- Store your journal entry in the shared BIOL368/F14:Class Journal Week 13 page. If this page does not exist yet, go ahead and create it.
- Link to the shared journal entry from your user page; this should be part of your template.
- Link the shared journal page to this assignment page.
- Sign your portion of the journal with the standard wiki signature shortcut (
- Add the "BIOL368/F14" category to the end of the wiki page (if someo
Now that you've done your own microarray analysis, we will revisit the case "Deception at Duke".
- What were the main issues with the data and analysis identified by Baggerly and Coombs? What best practices enumerated by DataONE were violated? Which of these did Dr. Baggerly claim were common issues?
- What recommendations does Dr. Baggerly recommend for reproducible research? How do these correspond to what DataONE recommends?
- Do you have any further reaction to this case after viewing Dr. Baggerly's talk?
- Look at the methods and results described in the paper from which you got the data you are working on. Do you think there is sufficient information there to reproduce their data analysis? Why or why not?