Dahlquist:Microarray Data Analysis Workflow

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This is the most current version of the data analysis protocol for the Dahlquist Lab microarray data. We will perform this analysis as a group during Week 1 of SURP 2015.

Summary of steps for microarray data analysis

1. Quantitate the fluorescence signal in each spot (GenePix Pro)
2. Calculate the ratio of red/green fluorescence (GenePix Pro)
3. Log transform the ratios (GenePix Pro)
4. Normalize the ratios on each microarray slide (within-chip normalization)
5. Normalize the ratios for a set of slides in an experiment (between-chip normalization)
6. Perform statistical analysis on the ratios
   • Within-strain ANOVA
   • Modified t test for each timepoint
   • Between-strain ANOVA
   • Benjamini & Hochberg and Bonferroni p value corrections for the above three tests
   • "Sanity Check" on above three tests
7. Pattern finding algorithms (clustering with stem)
8. Gene Ontology term enrichment analysis (on clusters with stem or on gene sets with MAPPFinder)
9. Pathway analysis (GenMAPP)
10. Determining candidate transcription factors and gene regulatory network (YEASTRACT)
11. Dynamical modeling with GRNmap; visualization with GRNsight

Before you begin...

Viewing File Extensions

• The Windows 7 operating systems defaults to hiding file extensions. To turn them back on, do the following:

  

  Folder Options window

  1. Go to the Start menu and select "Control Panel".
  2. In the window that appears, search for "Folder Options" in the search field in the upper right hand corner.
  3. Click on "Folder Options" in the main window.
  4. When the Folder Options window appears, click on the View tab.
  5. Uncheck the box for "Hide extensions for known file types".
  6. Click the OK button.
• The computers in Seaver 120 are are set to erase all custom user settings and restore the defaults once they have been restarted, so you will probably have to do this many times throughout the semester when using these computers.

Set Your Browser to Prompt You for the Location to Save your Downloaded Files

• In Mozilla Firefox, open the Options window.
  • Select the radio button that says "Always ask me where to save files".
  • You could also change the default "Save files to" location to your Desktop, so that will be the first choice when it prompts you where to save the file. (You will have to temporarily deselect the radio button to do this and then reselect it when you are done.
  • Click OK to save your changes.
• In Google Chrome, open the Settings window.
  • Click on the link at the bottom of the page that says "Advanced Settings".
  • Check the box that says "Ask where to save each file before downloading".
  • You could also change the default Download location to your Desktop, so that will be the first choice when it prompts you where to save the file.
  • Your settings are automatically saved.

Steps 1-3: Generating Log2 Ratios with GenePix Pro

• The protocol for gridding and generating the intensity (log2 ratio) data with GenePix Pro 6.1 is found on this page.
• This protocol will generate a *.gpr file for each chip which is then fed into the normalization protocol below.

Steps 4-5: Within- and Between-chip Normalization

• A more detailed protocol can be found on this page. An abbreviated protocol is summarized below.

Installing R 3.1.0 and the limma package

The following protocol was developed to normalize GCAT and Ontario DNA microarray chip data from the Dahlquist lab using the R Statistical Software and the limma package (part of the Bioconductor Project).

• The normalization procedure has been verified to work with version 3.1.0 of R released in April 2014 (link to download site) and and version 3.20.1 of the limma package ( direct link to download zipped file) on the Windows 7 platform.
  • Note that using other versions of R or the limma package might give different results.
  • Note also that using the 32-bit versus the 64-bit versions of R 3.1.0 will give different results for the normalization out in the 10^-13 or 10^-14 decimal place. The Dahlquist Lab is standardizing on using the 64-bit version of R.
• To install R for the first time, download and run the installer from the link above, accepting the default installation.
• To use the limma package, unzip the file and place the contents into a folder called "limma" in the library directory of the R program. If you accept the default location, that will be C:\Program Files\R\R-3.1.0\library (this will be different on the computers in S120 since you do not have administrator rights).

Running the Normalization Scripts

• Create a folder on your Desktop to store your files for the microarray analysis procedure.
• Download the zipped file that contains the .gpr files and save it to this folder (or move it if it saved in a different folder).
  • Unzip this file using 7-zip. Right-click on the file and select the menu item, "7-zip > Extract Here".
• Download the GCAT_Targets.csv file and Ontario_Targets_wt-dCIN5-dGLN3-dHAP4-dHMO1-dSWI4-dZAP1-Spar_20150514.csv files and save them to this folder (or move them if they saved to a different folder).
• Download the Ontario_Chip_Within-Array_Normalization_modified_20150514.R script and save (or move) it to this folder.
• Download the Within-Array_Normalization_GCAT_and_Merged_Ontario-GCAT_Between-Chip_Normalization_modified_20150514.R script and save (or move) it to this folder.

Within Array Normalization for the Ontario Chips

• Launch R x64 3.1.0 (make sure you are using the 64-bit version).
• Change the directory to the folder containing the targets file and the GPR files for the Ontario chips by selecting the menu item File > Change dir... and clicking on the appropriate directory. You will need to click on the + sign to drill down to the right directory. Once you have selected it, click OK.
  • In R, select the menu item File > Source R code..., and select the Ontario_Chip_Within-Array_Normalization_modified_20150514.R script.
  • You will be prompted by an Open dialog for the Ontario targets file. Select the file Ontario_Targets_wt-dCIN5-dGLN3-dHAP4-dHMO1-dSWI4-dZAP1-Spar_20150514.csv and click Open.
  • Wait while R processes your files.