Fall 2016

Principles of experimental design

  • Replication. It allows the experimenter to obtain an estimate of the experimental error
  • Randomization. It requires the experimenter to use a random choice of every factor that is not of interest but might influence the outcome of the experiment. Such factors are called nuisance factors
  • Blocking. Creating homogeneous blocks of data in which a nuisance factor is kept constant while the factor of interest is allowed to vary. Used to increase the accuracy with which the influence of the various factors is assessed in a given experiment
  • Block what you can, randomize what you cannot

Replicates

  • Technical replicates and Biological replicates
  • Rule of thumb: for two-fold change – use 3 replicates
  • Smaller change – 5 replicates

Randomization

  • Each gene has multiple probes – randomize their position on the slide

Blocking

  • Treatment and RNA extraction days are confounded

Blocking

  • Block replicated experiments

Pooling

Pooling

  • OK when the interest is not on the individual, but on common patterns across individuals (population characteristics)
  • Results in averaging -> reduces variability -> substantive features are easier to find
  • Recommended when fewer than 3 arrays are used in each condition
  • Beneficial when many subjects are pooled one pool vs independent samples in multiple pools

 

"inference for most genes was not affected by pooling"

C. Kendziorski, R. A. Irizarry, K.-S. Chen, J. D. Haag, and M. N. Gould. "On the utility of pooling biological samples in microarray experiments". PNAS March 2005, 102(12) 4252-4257

How to allocate the samples to microarrays?

  • which samples should be hybridized on the same slide?
  • how different experimental designs affect outcome?
  • what is the optimal design?

Example of four-array experiment

Dye swap

Common reference design

Loop design

Comparing the designs

Design with all direct pairwise comparisons