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Post-Genomics Experimental Design CSC8309 - Gene Expression and Proteomics Simon Cockell & Cedric Simillion

Dec 22, 2015

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  • Slide 1
  • Post-Genomics Experimental Design CSC8309 - Gene Expression and Proteomics Simon Cockell & Cedric Simillion
  • Slide 2
  • Outline Introduction Post-Genomic Technologies The Importance of Design Experimental Design When Design Goes Bad More Commonly Made Mistakes Things Done Right Types of Experiment
  • Slide 3
  • Post-Genomic Technologies Set of technologies that have become prevalent since the advent of genome sequencing Also referred to as functional genomics technologies Transcriptomics Proteomics Metabolomics 'High-throughput techniques, generate lots of data, fast
  • Slide 4
  • Importance of Design Functional Genomics experiments are expensive The quantity of data can mask interesting biological variation (noise) Bad design can increase noise Or at least fail to minimise it
  • Slide 5
  • When Design Goes Wrong A trivial example Bill and Ben want to identify proteins upregulated in response to water starvation in a drought resistant plant So, Bill went away and grew some plants, and so did Ben
  • Slide 6
  • When Design Goes Wrong continued Bill chose 3 plants, and Ben chose 4 Bill grew his at home in normal conditions, and Ben grew his in the lab with minimal water Then, after a few days of growth, they each took samples from their plants and ran 2D-PAGE
  • Slide 7
  • When Design Goes Wrong analysis They used average gels of the 2 groups of plants to find differentially expressed proteins They did t-tests for every spot on the gels, and found 400 of 2500 proteins (95% level) with significantly altered expression in drought conditions What now? They only wanted 10-20
  • Slide 8
  • When Design Goes Wrong What did they do wrong? Confounding Experiment cant distinguish between a number of factors: Drought Experimenter effects Difference between home and lab Selection Bill or Ben could be biased in how they selected plants, even unconsciously Randomised selection is preferred Unbalanced Better to have equal numbers in each group for many statistical analyses
  • Slide 9
  • When Design Goes Wrong How to improve Grow plants together under same conditions Select an equal number randomly for both Bill and Ben Both half their plants and grow normal and drought plants to the same protocol Better still, either Bill or Ben should do the whole experiment
  • Slide 10
  • When Design Goes Wrong Post mortem Even with a rigorously designed experiment, Bill and Ben may still have obtained confusing results It is common to identify many differentially expressed genes/proteins This can be a true reflection of the biology False discovery rate is necessarily high in post-genomic experiments, because of the number of hypotheses being tested Good experimental design could have reduced the complexity of their output providing a base for a robust statistical analysis of the data
  • Slide 11
  • Choice of Technology Microarray or proteomics? Affy or two-colour arrays? Reference sample? 2D gels or LC-MS? Single stain or DIGE? Reference sample? No easy (or correct) answers Depends very much on the individual experiment
  • Slide 12
  • Further Pitfalls Fahrenheit and the Cow Based on urban myth Still an important message No individual is typical Biological, as well as technical, replicates required
  • Slide 13
  • Further Pitfalls The pester problem Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad can I have a puppy, Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy? Dad, can I have a puppy ? Ask a question often enough, eventually youll get the answer youre after
  • Slide 14
  • Further Pitfalls The universe doesnt exist -- on average Pooling samples makes little sense: no information about distribution / need STDDEV for significance test My machine/technique is so accurate, I dont need replicates Accuracy has little effect on biological variance
  • Slide 15
  • Doing Things Right Some ideas for good design Blocking Replicates Calculating power
  • Slide 16
  • Doing Things Right Blocking Flask Gel IEF PAGE
  • Slide 17
  • Doing Things Right Replication
  • Slide 18
  • Doing Things Right Calculating power Probability density (null hypothesis) Probability density (alternative hypothesis) = probability of false positive (Type I Error) = Power 1- = probability of false negative (Type II Error)
  • Slide 19
  • Doing Things Right Calculating power Probability density (null hypothesis) Probability density (alternative hypothesis) = probability of false positive (Type I Error) = Power 1- = probability of false negative (Type II Error)
  • Slide 20
  • Doing Things Right Calculating power Probability density (null hypothesis) Probability density (alternative hypothesis) = probability of false positive (Type I Error) = Power 1- = probability of false negative (Type II Error)
  • Slide 21
  • Doing Things Right Calculating power Probability density (null hypothesis) Probability density (alternative hypothesis) = probability of false positive (Type I Error) = Power 1- = probability of false negative (Type II Error)
  • Slide 22
  • Types of Experiment Time course Cell cycle Following drug challenge Following external stimulus Following release of mutant Mutant vs Wild-Type Normal vs Diseased Developmental Changes Different Tissues Within cell differences
  • Slide 23
  • Types of Experiment Novel microarray techniques Genotyping SNP detection Copy Number Assessment Novel proteomics techniques High-throughput interaction detection Phosopho-proteomics Also Protein binding arrays Ligand binding arrays
  • Slide 24
  • A couple of quotes You know, the most amazing thing happened to me tonight. I was coming here, on the way to the lecture, and I came in through the parking lot. And you wont believe what happened. I saw a car with the license plate ARW 357. Can you imagine? Of all the millions of license plates in the state, what was the chance that I would see that particular one tonight? Amazing! Richard P. Feynman To consult a statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of. R.A.Fisher, 1938.
  • Slide 25
  • Summary Post-genomics technologies are powerful, but expensive Good design gives maximum return for minimum effort
  • Slide 26
  • Any questions? After the fact questions: [email protected]