SBC322: Experimental Design for Ecological & Evolutionary
Genomics
@yannick__ http://yannick.poulet.org
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?
Main potential problems
• Insufficient data/power
• Pseudoreplication
• Confounding factors
• Inappropriate statistics
Wrong Inaccurate & Misleading
Inappropriate design
Inappropriate implementation
Inappropriate analysis
(inappropriate interpretation)
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” conditionsdo all that you can to ensure your design is robust
Overall
• Avoid easy mistakes
• Design & statistics are closely interlinked
• Consider biology carefully
• Better to spend more time planning.
So what about eco/evo-omics?
It’s harder.
Reference genome sequencing/assembling?
What can go wrong with:
Gene expression experiments?
What can go wrong with:
John’s messed up experimentSocial form completely associated to Gp-9 locus
BB BB Bb
x x x
Single queen form Multiple queen form(>15% ) (< 5% )
Field collections?
What can go wrong with:
Surveying population diversity?or comparing populations?
What can go wrong with:
Identifying genes responsible for trait?
What can go wrong with:
Topics for next week
https://etherpad.mozilla.org/MQ1FNA2Rjm
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