Supervised microarray data analysis

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Supervised microarray data analysis

• Mark van de Wiel

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Quality control

• Protocols
• Perform a small scale, well-controlled experiment
  to assess influence of experimental factors
  (Microarrays from different batches, printing
  tips, dyes, linearity of the scanner, etc.)
• Continuous factors (temperature, humidity,
  spotsize over time, intensity of control spot
  over time) can be monitored with standard control
  chart techniques.

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Design of the experiment

• Think very, very well what the biological goals
  are.
• What software do you have at your disposal to
  analyse the data?
• Do we need reference or not?
• Biological design what tissues to combine on
  an array (cDNA)? More than one biological factor
  factorial design
• Dye-bias dye-swap.
• Design on the array (negative/positive controls,
  repeats?, how many genes? Pilot study first,
  distributing the repeats over experimental
  factors (spatial, printing tips, etc.))
• Save some space on the (cDNA) microarray for
  assessing variability due to experimental factors
  (e.g. print same control gene with several
  printing tips)

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Analysis Multiple testing (after normalization)

• Objective control the number of falsely selected
  genes
• FWE Family wise error rate
• Weak FWE control
  
  P(falsely select gene i, i1, ..., 20.000
  no gene truly expressed) ? ?
• Strong FWE control
  
  P(falsely select gene i, i1, ..., 20.000
  some genes expressed, some genes not expressed) ?
  ?
• FDR False Discovery Rate
• F Expected number of false rejections when no
  genes are expressed, T Total number of
  rejections
• FDR control F/T ? ?

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Multiple testing FWE vs FDR

• Control of FDR implies weak control of FWE
• Advantage strong control of the FWE
  significance level ? under all situations
  controlled
• Disadvantage less power than FDR control
• FWE based procedures tend to select less genes
  than FDR based procedure
• Software
• Bioconductor Step-down Westfall-Young (Dudoit
  et al.), control FDR and FWE.
• SAM (permutation based control of FDR)

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SAM

• Developed at Stanford, Tibshirani et al. (Paper
  Tusher et al, PNAS 98, 5116-5121) Claim is
  FDR-control
• Plus
• Ease of use, add-in to Excel
• Allows asymmetric cut-offs
• Minus
• Distribution under the null-hypotheses (no
  expression) needs to be the same for all genes
  to guarantee FDR control
• Combination with k-fold rule no control of FDR
  anymore
• Solutions Use (normal) rank scores and a simple
  rank statistic
• Explicitly test on k-fold expression
  combine with FDR criterion

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Modelling vs Normalisation Testing

• Modelling forces you to state what the
  assumptions are (linearity, normality,
  independence, etc.)
• Normalisation steps may not be commutative
• Non-linearities can be dealt with by
  normalisation methods
• Advanced modelling requires help of
  statistician/bio-informatician
• Standard approach to modelling ANOVA. Model has
  two levels
• Normalisation level which includes linear
  corrections for dye and microarray effects
• Gene expression level which includes effects on
  gene level, including interactions (interaction
  of interest is usually genevariety)

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Software

• Freeware SAM, Bioconductor
• Specialized commercial software Spotfire,
  Genespring, Genesight, Rosetta
• Most contain normalisation, variance stabilizing
  transformations, ANOVA, testing (most do not yet
  include the advanced multiple testing criteria)
• Statistical software SAS, S-Plus, SPSS
• Much more debugged, long history, better
  documentation (Often very unclear what the
  specialized packages really do.)
• Advantages specialized software user-friendly,
  visualisation (nice pictures), link with data
  bases, annotation
• Try several!!!

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Bayesian models

• Natural translation to networks (pathways)
• Complex models (linearity is not necessary,
  interactions)
• Prior biological knowledge can be included
• Nesting of the models (image analysis
  normalisation gene expression)
• Inference for complex functions of gene
  expression data is relatively easy
• No easy software
• Computational methods may take time to find
  reliable estimates
• Example Network

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Validation

• Cross-validation leave some data out and see how
  well the data values are predicted by the model
  (Note that for normalisation procedures it may be
  harder to predict the data from the normalized
  data)
• Biological validation (spikes known
  concentrations)
• Very useful for validating the normalisation
  procedure or the model
• Pretend that spikes with equal concentrations
  that are used under different conditions
  (different dyes, microarray batch)are different
  quantities.
• Estimate ratio of two estimates after
  normalisation or modelling
• Ratio should approximately be equal to 1.

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Comparison and meta analysis

• Objective comparisons between methods very much
  needed!
• Simulations may help (because we know the truth
  then). Setting up realistic simulations may be
  hard!
• Competition between several methods (CAMDA 03
  Lung cancer)
• Future goals
• Methods that allow for combining data from
  several experiments.
• From relative quantities to absolute quantities.
  
• Absolute quantities allow for direct comparison
  between labs. (otherwise, only if labs have used
  same reference material etc.)

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Useful overview papers, books

• Design Churchill, G.A. (2002) Fundamental of
  experimental design for cDNA microarrays. Nature
  Genet.32 (490-495)
• Analysis Slonim, D.K. (2002) From patterns to
  pathways gene expression data analysis comes of
  age Nature Genet.32 (502-508)
• Normalisation Quackenbush, J. (2002) Microarray
  normalisation and transformation Nature Genet.32
  (496-501)
• Pitfalls Richard Simon et al. (2003) Pitfalls in
  the Use of DNA Microarray Data for Diagnostic and
  Prognostic Classification J Natl Cancer Inst 95
  14-18.
• Books Baldi Hatfield (2002), DNA Microarrays
  and Gene expression, Cambridge University Press
• Speed, T. (2003) Statistical Analysis of Gene
  Expression Microarray DataChapman Hall
• Acknowledgement Nicola Armstrong (EURANDOM)