Some views on microarray experimental design

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Some views on microarray experimental design

• Rainer Breitling
• Molecular Plant Science Group Bioinformatics
  Research Centre
• University of Glasgow, Scotland, UK

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Personal Background

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

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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?

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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

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How many replicates?

• as many as possible
• Statistics says The more replicates, the better
  your estimate of expression (thats an asymptotic
  process, so if you add at least a few replicates,
  the effect will be really strong)

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How many replicates?

• a significance level (probability of detecting
  FP)
• 1-ß power to detect differences (probability of
  detecting TP)
• s standard deviation of the log-ratios
• d detectable difference between class mean
  log-ratios
• z percentile of standard normal distribution
• ? n required number of arrays (reference design)

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How many replicates?

• Five
• Experience shows For most common experiments you
  get a reasonable list of differentially expressed
  genes with 5 replicates

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How many replicates?

• Three
• One to convince yourself, one to convince your
  boss, one just in case...

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How many replicates?

• It depends on
• the quality of the sample
• the magnitude of the expected effect
• the experimental design
• the method of analysis

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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

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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!

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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

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The experimental design

• Three major options
• reference design (flexible)
• balanced block design (efficient)
• loop design (elegant)

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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
C
D
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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

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The method of analysis
  0h 9.5h 11.5h 13.5h 15.5h 18.5h 20.5h
    6144 - purine base metabolism 6099 - tricarboxylic acid cycle 6099 - tricarboxylic acid cycle 3773 - heat shock protein activity 6099 - tricarboxylic acid cycle
      9277 - cell wall (sensu Fungi) 3773 - heat shock protein activity 5749 - respiratory chain complex II (sensu Eukarya) 6099 - tricarboxylic acid cycle 3773 - heat shock protein activity
      297 - spermine transporter activity 6950 - response to stress 6121 - oxidative phosphorylation, succinate to ubiquinone 5977 - glycogen metabolism 5749 - respiratory chain complex II (sensu Eukarya)
      15846 - polyamine transport 297 - spermine transporter activity 8177 - succinate dehydrogenase (ubiquinone) activity 6950 - response to stress 6121 - oxidative phosphorylation, succinate to ubiquinone
        4373 - glycogen (starch) synthase activity 3773 - heat shock protein activity 4373 - glycogen (starch) synthase activity 8177 - succinate dehydrogenase (ubiquinone) activity
        15846 - polyamine transport 4373 - glycogen (starch) synthase activity 4129 - cytochrome c oxidase activity 6537 - glutamate biosynthesis
        5353 - fructose transporter activity 7039 - vacuolar protein catabolism 5751 - respiratory chain complex IV (sensu Eukarya) 6097 - glyoxylate cycle
        15578 - mannose transporter activity 6950 - response to stress 5749 - respiratory chain complex II (sensu Eukarya) 5750 - respiratory chain complex III (sensu Eukarya)
        7039 - vacuolar protein catabolism 4129 - cytochrome c oxidase activity 6121 - oxidative phosphorylation, succinate to ubiquinone 9060 - aerobic respiration
        8645 - hexose transport 5751 - respiratory chain complex IV (sensu Eukarya) 8177 - succinate dehydrogenase (ubiquinone) activity 4129 - cytochrome c oxidase activity
iterative GroupAnalysis (iGA)
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respiratory chain complex II
glyoxylate cycle
citrate (TCA) cycle
oxidative phosphorylation (complex V)
Graph-based iterative GroupAnalysis (GiGA)
respiratory chain complex III
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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

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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

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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...

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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

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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/rb1
  06x/microarray_tips.htm

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Contact
Rainer Breitling Bioinformatics Research
Centre Davidson Building A416 R.Breitling_at_bio.gla.
ac.uk http//www.brc.dcs.gla.ac.uk/rb106x