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Microarray Pitfalls

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Microarray Pitfalls Stem Cell Network Microarray Course, Unit 3 October 2006 Goals To provide some guidelines on Affymetrix microarrays: How to use them How not to ... – PowerPoint PPT presentation

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Title: Microarray Pitfalls


1
Microarray Pitfalls
  • Stem Cell Network
  • Microarray Course, Unit 3
  • October 2006

2
Goals
  • To provide some guidelines on Affymetrix
    microarrays
  • How to use them
  • How not to use them
  • Things to keep in mind when designing experiments
    and analyzing data
  • This is a general discussion of issues and is by
    no means exhaustive

3
Inconsistent Annotations
  • Affymetrix provided probeset annotations change
    over time
  • The gene symbol associated with a given probeset
    is not necessarily stable
  • This is due to changes in gene prediction as new
    information becomes available.

4
Inconsistent Annotations (2)
An inconsistently annotated probeset
  • Perez-Iratxeta, C. and M.A. Andrade. 2005.
    Inconsistencies over time in 5 of NetAffx
    probe-to-gene annotations. BMC Bioinformatics. 6,
    183.
  • 5 of probesets have gene identifiers that change
    over the two year time span covered by this
    analysis

5
Inconsistent Annotations (3)
  • How do we deal with this?
  • Always note annotation version used in analysis
    especially when it is for publication
  • Report probeset name as well as gene symbol
  • Remember that re-analysis with later annotations
    may yield different results
  • Keep your annotation files up to date

6
Old chips, new data
  • Expression microarrays are designed based the
    best available model of the genome of interest
  • The model for the HG-U133 microarrays was a human
    genome assembly that was only 25 complete!
  • The human assembly is gt99 complete now

7
Old chips, new data (2)
  • How do we deal with this?
  • A number of groups provide re-mappings of probes
    to probesets based upon the latest data
    available, for example
  • Dai M, et al. Evolving gene/transcript
    definitions significantly alter the
    interpretation of GeneChip data. Nucleic Acids
    Res. 200533e175

8
Multiple Testing Corrections
  • A single expression microarray experiment
    actually consist of hundreds of thousands of
    simultaneous parallel experiment
  • This means you can test many hypotheses
    simultaneously
  • This is not free the significance of any given
    result is decreases as a function of the number
    of hypotheses tested

9
Multiple Testing Corrections (2)
  • How do we deal with this?
  • Limit the number of hypothesis you are testing
    instead of just fishing in the whole data set.
  • Do this by selecting a set of candidate genes
    ahead of time based on your knowledge of the
    biology of the system.

10
Multiple Testing (3)
  • Sandrine Dudoit, Juliet Popper Shaffer and
    Jennifer C. Boldrick Multiple Hypothesis Testing
    in Microarray Experiments Statistical Science
    2003, Vol. 18, No. 1, 71103
  • The biological question of differential
    expression can be restated as a problem in
    multiple hypothesis testing the simultaneous
    test for each gene of the null hypothesis of no
    association between the expression levels and the
    responses
  • Talk to a statistician if you have doubts

11
Not everything is in the array
  • Probesets are designed with a bias towards the 3
    end of the gene.
  • they wont distinct splice variants
  • wont pick up alternative 3 endings

12
Not everything is in the array (2)
  • What can we do about this?
  • You should be aware of this, but not much can be
    done.
  • Use other technologies to complement your
    microarray results (PCR, sequencing)

13
What are you measuring?
  • Remember that you are detecting the average mRNA
    over a population of cells.
  • Is your sample homogenous?
  • If its not homogenous then what are you
    measuring? How many types of cells in what state?
  • Time series of differentiating cells are
    particularly problematic.

14
Inhomogenous Samples?
  • Many sources of inhomogeneity
  • Source organism gender
  • Cell cycle
  • Tissue source
  • Diet
  • Some can be eliminated
  • All should be documented where possible

15
Chips dont detect protein
  • Central assumption of microarray analysis The
    level of mRNA is positively correlated with
    protein expression levels.
  • Higher mRNA levels mean higher protein
    expression, lower mRNA means lower protein
    expression
  • Other factors
  • Protein degradation, mRNA degradation,
    polyadenylation, codon preference, translation
    rates,.

16
Conclusion
  • This is a general discussion of issues, doesnt
    cover all pitfalls.
  • Please contact ogicinfo_at_ohri.ca if you have any
    comments, corrections or questions.
  • See associated bibliography for references from
    this presentation and further reading.
  • Thanks for your attention!
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