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!