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Some views on microarray experimental design

Some views on microarray experimental design. Rainer Breitling Molecular Plant Science Group & Bioinformatics Research Centre University of Glasgow, Scotland, UK. Personal Background. University of Glasgow, Scotland, UK Molecular Plant Sciences Group Bioinformatics Research Centre

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Some views on microarray experimental design

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  1. Some views on microarray experimental design Rainer Breitling Molecular Plant Science Group & Bioinformatics Research Centre University of Glasgow, Scotland, UK

  2. Personal Background • University of Glasgow, Scotland, UK • Molecular Plant Sciences Group • Bioinformatics Research Centre • Functional Genomics Facility

  3. Some common questions in microarray experimental design • How many arrays will I need? • Should I pool my samples? • Which arrays should I choose? • Which samples should I put together on one array?

  4. Why are microarrays special? • produce large amounts of data instantaneously • can look for unexpected effects • are still quite expensive almost never repeated careful design necessary before you start

  5. How many replicates? • as many as possible Statistics says: The more replicates, the better your estimate of expression (that’s an asymptotic process, so if you add at least a few replicates, the effect will be really strong)

  6. How many replicates? • α significance level (probability of detecting FP) • 1-β power to detect differences (probability of detecting TP) • σ standard deviation of the log-ratios • δ detectable difference between class mean log-ratios • z percentile of standard normal distribution •  n required number of arrays (reference design)

  7. How many replicates? • Five Experience shows: For most common experiments you get a reasonable list of differentially expressed genes with 5 replicates

  8. How many replicates? • Three One to convince yourself, one to convince your boss, one just in case...

  9. How many replicates? • It depends on • the quality of the sample • the magnitude of the expected effect • the experimental design • the method of analysis

  10. The quality of the sample • smaller samples (single cells) are more noisy than large samples (tissue homogenates) • cell cultures are less noisy than patient biopsies • sample pooling can decrease noise – if individual variation is not of interest

  11. The magnitude of the effect • Microarrays are very sensitive • To keep effects small: • use early time points, gentle stimuli • never compare dogs and donuts • if you get a list of 2000 genes that are significantly changed, your experiment failed!

  12. The magnitude of the effect • some problematic cases • stably transfected cell lines (are they still the same cells?) • knock-out organisms (even the same tissue can be a different) • local changes may be diluted  cell isolation will increase noise

  13. The experimental design • Three major options: • reference design (flexible) • balanced block design (efficient) • loop design (elegant)

  14. The experimental design • loop designs can save samples... • ...but they can cause interpretation nightmares in less simple cases (use for large studies, if you have a full-time statistician in the team) B C D A A B R R R R D C

  15. The method of analysis • Golub et al. (1999) data set • 38 leukemia patient bone marrow samples, hybridized individually to Affymetrix microarrays • Differential expression between two leukemia types was examined, using random subsets of the complete dataset

  16. The method of analysis iterative GroupAnalysis (iGA)

  17. respiratory chain complex II glyoxylate cycle citrate (TCA) cycle oxidative phosphorylation (complex V) Graph-based iterative GroupAnalysis (GiGA) respiratory chain complex III

  18. What is a good replicate? The experiment your competitor at the other side of the globe would do to see if your results are reproducible • Vary “all” parameters – challenge your results • Prepare new samples, from new cultures, using new buffers and new graduate students • Remember to produce matched controls

  19. What is a “bad” replicate? • technical replicates (i.e. hybridizing the same sample repeatedly) • dye-swapping experiments (usually gene-specific dye bias is not a big issue, and dye balancing is more efficient anyway) • pooled samples, hybridized repeatedly • the same preparation, only labelled twice

  20. Should samples be pooled? • most samples are already pooled – they come from multiple cells • pool to increase amount of mRNA, but only as much as necessary • prepare independent pools to assess variation • problems: bias, “contamination”, outliers, information loss...

  21. Which arrays are the best? • Standard arrays compare and exchange data easily • Whole-genome arrays detect unexpected effects, increase confidence • Single-color arrays (Affymetrix GeneChip) for more complex comparisons • Annotated arrays

  22. Further reading • Dobbin, Shih & Simon (2003) J. Natl. Cancer Inst. 95: 1362. • Yang & Speed (2002) Nature Rev. Genet. 3: 579. • Breitling (2004) http://www.brc.dcs.gla.ac.uk/~rb106x/microarray_tips.htm

  23. Contact Rainer Breitling Bioinformatics Research Centre Davidson Building A416 R.Breitling@bio.gla.ac.uk http://www.brc.dcs.gla.ac.uk/~rb106x

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