Microarray analysis exercises 2

WIBR Microarray Analysis Course - 2007

Starting Data     Processed Data

Class 2 exercises

Part IV. Identifying differentially expressed genes

  1. Differentially expressed genes can be naively determined by fold changes but more effectively determined by using a statistic such as the t test.
  2. We'll compare the results of these two methods later in Part VI.
  3. Use the t test with each gene to determine if the data on fetal and adult expression are different in the brain and/or liver.
  4. Use the "Absent/Present" calls from the Affymetrix algorithm to flag genes with questionable expression levels.
  5. Sort data and remove non-expressed probesets.
  6. (Optional) Correct t-test p-values for multiple hypothesis testing by calculating the False Discovery Rate (FDR)
  7. List all the gene IDs for those that meet your significance threshold (such as raw p < 0.01) and are present in at least one sample.
  8. Compile all the log-transformed data for all genes that show a significant change in expression (to use later for clustering) in the four tissues.
  9. Optional: The BaRC submatrix selector, an alternative to Excel's VLOOKUP

Part V. Clustering

  1. Use any or all of these data sets. The third dataset, being across more tissues, may be the most interesting.
    1. your subset of log2-transformed expression values (the "selected" sheet, from Part IV.8), which you should have saved as a tab-delimited text file. A similar dataset is also found here.
    2. a full set of expression ratios (transformed to log base 2), with values compared to the mean across all tissues
  2. Open Cluster 3.0, a clustering application that works on all operating systems. It's an enhanced version of the Eisen clustering program. See the manual for more information about the program.
  3. File > Open and select your file of expression data (one of the files in Part V.1).
  4. Note that there are some filtering and normalization functions on the tabs "Filter Data" and "Adjust Data", but we've already performed these steps.
  5. Try Hierarchical clustering using the default settings.
  6. Open JavaTreeView for visualizing your data as a heatmap.
  7. Try k-Means clustering using the default settings.
  8. Optional: While in JavaTreeView, try Export > Export to Postscript and save all or part of your figure. This will produce an image of optimal resolution. Otherwise, you may wish to export to GIF or bitmap (which are easier to handle in Photoshop, but lower resolution).
  9. Optional: Open the heatmap in Illustrator or Photoshop.

WIBR Microarray Analysis Course - 2007