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