Basics on microarrays

1
Basics on microarrays

• Concepts of cDNA, mRNA, dyes, probes,
  hybridization,
• cDNA array, Affymatrix array
• How to resolve the errors and bias introduced by
  the microarray experiment
• Typical pre-processing procedures
• Procedures specially designed for cDNA and
  Affymatrix
• Procedure common to both platforms

2
Analysis of differentially expressed genes

• Parametric statistics t-tests
• Paired or one-sample t-test
• Unpaired or two-sample t-test
• Bootstrap analyses for sample distribution
• Calculation of p-value from bootstrap procedure
• Multiplicity of testing performing statistical
  tests on many genes in parallel
• Bonferroni adjustment
• Procedure in SAM -- basic idea
• GO term enrichment basic idea

3
Machine learning and data mining

• Unsupervised learning methods
• Hierarchical clustering algorithms
• K-mean
• Principal component analysis
• Clustering quality assessment reliability
• Supervised classification
• Perceptron algorithm
• K-nearest neighbor
• Fishers discriminant (the difference with PCA)
• Bayesian decision rule
• Support vector machines (the relationship with
  perceptron algorithm)
• Feature selection
• Classification Cross-validation
• criteria
• Procedure for supervised classification (learning
  and testing, evaluation criteria,
  cross-validation)
• For the above, you should be able to explain
  principle mathematically and perform computation

4

• Transcription bind motif detection
• Purpose, general idea
• Bayesian network learning
• Purpose, general idea