1. Dahlia Nielsen North Carolina State University Bioinformatics Research Center

2. Microarray Animation http://www.bio.davidson.edu/Courses/ genomics/chip/chip.html

3. Importing data into JMP/Genomics Need two (paired) tables  Data: expression intensities  Experimental design Data probably originally exists in separate files:  one file per sample/microarray  first create experimental design file

4. Experimental Design File Required Columns  columnname  file  Array (can be “made up” values)  intensity if using text file input  dye (or channel) if two-color platform cy3 vs cy5

5. Experimental Design File Required Columns Other columns  information about samples treatment class phenotype …

6. Data Analysis Steps QC  distribution analysis  correlation plots Normalization more QC  same as above Analysis Results visualization

7. Data Analysis Steps QC  distribution analysis  correlation plots Normalization more QC  same as above Analysis Results visualization JMP/Genomics creates a script for each of these can run script to re- create results (without re-doing analyses)

8. QC Distribution analysis  visualization of how consistent your data/samples are  useful for detecting problem arrays Correlation plots  also a measure of array consistency

9. Normalization Lots of choices Lots of discussion No right / wrong Depends in part on your goals Different degrees  very “light” (mixed model)  intermediate (loess)  more “heavy-handed” (quantile)

10. More QC Indication of success of normalization procedure as before …  consistency between arrays/samples  detect problem arrays

11. Analysis Generally performed one gene at a time Hypothesis-testing framework  ANOVA (test for changes in expression levels across treatment groups)  multiple-testing adjustment necessary Exploratory procedures  pca  cluster analysis

12. Volcano plots Visualization tool to display results plot of effect size (x-axis) vs. significance level (y-axis) Some genes may display large differences between treatment groups, but also high variance (less significance) Some genes might display smaller effect sizes, but expression values very consistent (low var.) … smaller p-values

13. Final results Probably should consider not only pvalues, but also magnitude of effect small changes (in spite of small pvalues) might not be replicable  inherent accuracy of microarrays  tendency of performing experiments with small sample sizes

14. Final check on results Once identify genes with significant results  e.g. expression levels significantly different between treatment groups Examine data  Is the change identified (above) readily apparent?  Normalized data …  And raw data