Difference between revisions of "Week 8"
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| − | #* Steps 4-5 was performed for you using a script in R, a statistics package (see: [ | + | #* Steps 4-5 was performed for you using a script in R, a statistics package (see: [https://openwetware.org/wiki/Dahlquist:Microarray_Data_Analysis_Workflow#Steps_4-5:_Within-_and_Between-chip_Normalization Microarray Data Analysis Workflow]) |
#* You will perform the following steps: | #* You will perform the following steps: | ||
#Perform statistical analysis on the ratios | #Perform statistical analysis on the ratios | ||
Revision as of 18:02, 17 October 2017
This journal entry is due on Tuesday, October 24, at 12:01 AM PDT.
Contents
Objectives
The purpose of this assignment is:
- to conduct the "analyze" step of the data life cycle for a DNA microarray dataset.
- to keep a detailed electronic laboratory notebook to facilitate reproducible research.
- to revisit the "Deception at Duke" case with new insights because you have analyzed your own dataset.
Individual Journal Assignment
- Store this journal entry as "username Week 8" (i.e., this is the text to place between the square brackets when you link to this page).
- Invoke your template on your journal entry page so that you:
- Link from your journal entry page to this Assignment page.
- Link from your journal entry to your user page.
- Add the "Journal Entry" category to the end of your wiki page.
- Because you have invoked your template on your user page, you should also have a:
- Link from your user page to this Assignment page.
- Link to your journal entry from your user page.
- Include both the Acknowledgments and References section as specified by the Week 1 assignment.
- For your assignment this week, the electronic laboratory notebook you will keep on your individual wiki page is crucial. An electronic laboratory notebook records all the manipulations you perform on the data and the answers to the questions throughout the protocol. Like a paper lab notebook found in a wet lab, it should contain enough information so that you or someone else could reproduce what you did using only the information from the notebook.
- We will be performing a series of computations on a microarray dataset, primarily using Microsoft Excel. In the interests of reproducible research, it is appropriate to copy and paste the methods from this assignment into your individual journal entry.
- You must then modify the general instructions (which are generic to the whole class) to your own data analysis, recording the specific modifications and equations that you used on your dataset.
- Record the answers to the questions posed in the protocol at the place in which they appear in the method. You do not need to separate them out in a different results section.
- All files generated in the protocol must be uploaded to the wiki and linked to from your journal entry page.
- You will write a summary paragraph that gives the conclusions from this week's analysis.
Homework Partners
Homework partners for this week are listed below. The particular dataset that you and your partner will work on is also indicated below. You are expected to consult with your partner, sharing your domain expertise, in order to complete the assignment. However, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed). You must give the details of the interaction with your partner in the Acknowledgments section of your journal assignment.
- Eddie Azinge, Emma Tyrnauer: wild type data
- Eddie Bachoura, Quinn Lanners: dASH1 data
- Mary Balducci, Simon Wroblewski: dCIN5 data
- Dina Bashoura, Zach Van Ysseldyk: dGLN3 data
- Blair Hamilton, Nicole Kalcic: dHAP4 data
- Hayden Hinsch, Arash Lari: dHMO1 data
- John Lopez, Corinne Wong: dSWI4 data
- Antonio Porras, Katie Wright: dZAP1 data
Microarray Data Analysis
We will be working on the protocols in class on Tuesday, October 17 and Thursday, October 19. Whatever you do not finish in class will be homework to be completed by the Week 8 journal deadline.
Background
This is a list of steps required to analyze DNA microarray data.
- Quantitate the fluorescence signal in each spot
- Calculate the ratio of red/green fluorescence
- Log2 transform the ratios
- Steps 1-3 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
- Normalize the ratios on each microarray slide
- Normalize the ratios for a set of slides in an experiment
- Steps 4-5 was performed for you using a script in R, a statistics package (see: Microarray Data Analysis Workflow)
- You will perform the following steps:
- Perform statistical analysis on the ratios
- Compare individual genes with known data
- Steps 6-7 are performed in Microsoft Excel
- Pattern finding algorithms (clustering)
- Map onto biological pathways
- We will use software called STEM for the clustering and mapping
- Identifying regulatory transcription factors responsible for observed changes in gene expression
- Dynamical systems modeling of the gene regulatory network (GRNmap)
- Viewing modeling results in GRNsight
- Store your journal entry in the shared Class Journal Week 8 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first 👏🏼)
- Link to your journal entry from your user page.
- Link back from the journal entry to your user page.
- NOTE: You can easily fulfill the links part of these instructions by adding them to your template and using the template on your user page.
- Sign your portion of the journal with the standard wiki signature shortcut (
~~~~). - Add the "Journal Entry" and "Shared" categories to the end of the wiki page (if someone has not already done so).
View
Now that you've done your own microarray data analysis, we will revisit the case "Deception at Duke".
- View the video: The Importance of Reproducible Research in High-Throughput Biology: Case Studies in Forensic Bioinformatics.
- View the slides from DataONE on data entry and manipulation.
- Optional: for more information on the Duke saga, see the web site put together by Baggerly and Coombes here.
Reflect
- 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?
- What best practices did you perform for this week's assignment?
- Do you have any further reaction to this case after viewing Dr. Baggerly's talk?
