Practical microarray analysis – experimental design
Heidelberg, October 2003
1
Design of microarray experiments
Ulrich Mansmann
mansmann@imbi.uni-heidelberg.de
Practical microarray analysis
October 2003
Heidelberg
Practical microarray analysis – experimental design
Heidelberg, October 2003
2
Experiments
Scientists deal mostly with experiments of the following form:
• A number of alternative conditions / treatments
• one of which is applied to each experimental unit
• an observation (or several observations) then being made on each unit.
The objective is:
• Separate out differences between the conditions / treatments from the
uncontrolled variation that is assumed to be present.
• Take steps towards understanding the phenomena under investigation.
Practical microarray analysis – experimental design
Heidelberg, October 2003
3
Statistical thinking
Uncertain
knowledge
Knowledge of the
extent of
uncertainty in it
Useable
knowledge
+
=
Measurement model
m = µ + e
m – measurement with error, µ - true but unknown value
What is the mean of e?
What is the variance of e?
Is there dependence between e and µ?
What is the distribution of e (and µ)?
Typically but not always: e ~ N(0,σ²)
Gaussian / Normal measurement model
Decisions on the
experimental design
influence the
measurement model.
Practical microarray analysis – experimental design
Heidelberg, October 2003
4
Main requirements for experiments
Once the conditions / treatments, experimental units, and the nature of the observations
have been fixed, the main requirements are:
• Experimental units receiving different treatments should differ in no systematic
way from one another – Assumptions that certain sources of variation are absent
or negligible should, as far as practical, be avoided;
• Random errors of estimation should be suitably small, and this should be achieved
with as few experimental units as possible;
• The conclusions of the experiment should have a wide range of validity;
• The experiment should be simple in design and analysis;
• A proper statistical an