1. SBC322: Experimental Design
for Ecological & Evolutionary
Genomics
@yannick__ http://yannick.poulet.org

3. Why consider experimental design?
• If you’re performing experiments
• Cost
• Time
• for experiment
• for analysis
• Ethics
• If you’re deciding to fund? to buy? to approve? to compete?
• are the results real?
• allow clear interpretation?
• can you trust the data?

4. Main potential problems
• Insufficient data/power
• Pseudoreplication
• Confounding factors
• Inappropriate statistics
Inappropriate design
Inappropriate implementation
Inappropriate analysis
(inappropriate interpretation)
Inaccurate & Wrong
Misleading

5. Taking measurements
• How do you calibrate measuring instruments
(including human observers)?
• Steps to reduce:
• subjective decision making?
• inter-observer variability?
• intra-observer variability?
• Unusable/illegible measurements/notes
• Automation?
• Avoid floor & ceiling effects
• Ensuring that subjects are in “natural” conditions
do all that you can to ensure your design is robust

6. Overall
• Avoid easy mistakes
• Design & statistics are closely interlinked
• Consider biology carefully
• Better to spend more time planning.

8. So what about eco/evo-omics?

9. It’s harder.

10. What can go wrong with:
Reference genome
sequencing/assembling?

11. What can go wrong with:
Gene expression experiments?

12. John’s messed up experiment
Social form completely associated to Gp-9 locus
Single queen form Multiple queen form
(< 5% ) (>15% )
BB BB Bb
x x x

13. What can go wrong with:
Field collections?

14. What can go wrong with:
Surveying population diversity?
or comparing populations?

15. What can go wrong with:
Identifying genes responsible for
trait?

17. Topics for next week
https://etherpad.mozilla.org/
MQ1FNA2Rjm