Statistical Issues in the Design of Microarray Experiments

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Statistical Issues in the Design of Microarray
Experiments

• Lara Lusa
• U.O. Statistica Medica e Biometria
• Istituto Nazionale per lo Studio e la Cura dei
  Tumori, Milano
• NETTAB 2003
• Bologna, 28th November 2003

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Outline

• Biostatistics and microarrays
• Study objectives
• Design of microarray experiments
• A case study a designed experiment

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Biostatistics and microarrays

• Microarray research unique challenge for
  interdisciplinary collaboration
• Can biostatisticians be useful in microarray
  research?
• Are available software tools a valid substitution
  for collaboration with biostatisticians?

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What can biostatisticians do?

• Active collaboration with researchers from
  biomedical and bioinformatics fields
• to develop and critically evaluate methods for
• design of microarray experiments
• analysis of data
• to perform data-analysis
• to develop software tools and train biomedical
  researchers to use them

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Italian inter-university research group

• Statistical issues in design and analysis of
  microarray data
• MIUR grant 2003-2005
• Firenze (Annibale Biggeri)
• Milano (Giuseppe Gallus)
• Padova (Monica Chiogna)
• Torino (Mauro Gasparini)
• Udine (Corrado Lagazio)

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Collaborations

• Milano
• Istituto Nazionale per lo Studio e la Cura dei
  Tumori, Milano (statisticians, biologists,
  molecular oncologists)
• IFOM, Milano (biologists, bioinformatics)
• Biometric Research Branch, NCI, Bethesda
  (statisticians)
• Edo Tempia Foundation, Biella
• Bioconductor poject (software development)

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Study objectives

• Class comparison (supervised)
• establish differences in gene expression between
  predetermined classes
• Class prediction (supervised)
• prediction of phenotype using gene expression
  data
• Class discovery (unsupervised)
• discover groups of samples or genes with similar
  expression

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Design of microarray experiments

• Design of arrays
• Allocation of samples
• Replication
• Labeling of samples (cDNA)
• reference design
• balanced block design
• loop design

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Levels of replication

• Biological replicates
• multiple samples from different populations
• Technical replicates
• multiple samples from the same subject
• multiple samples from the same mRNA
• multiple clones or probes of the same gene on the
  array

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How many replicates?

• Biological replicates essential to make inference
  about population
• Technical replicates useful for quality control
  and for increasing precision
• How to determine sample size?
• Problem-dependent
• simple methods available for class comparison
  problems
• not yet clear what to use for class discovery

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Common pitfalls in microarray experiments

• Too little or no replication
• Use of replication at the wrong level
• Experiments with cell lines assuming no
  variability among cell lines of the same type
• Inappropriate use of pooling
• ok use of multiple independent pools
• but is it useful?
• individual information lost

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Case study a designed experiment

• Biological aim assess the effect of Toremifen on
  MCF-7 breast cancer cell line, in terms of gene
  expression

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Week 1
POOL
POOL
CDNA
Affymetrix
CDNA
Affymetrix
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Week 2 and 3
POOL
POOL
CDNA
Affymetrix
CDNA
Affymetrix
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Statistical aims

• comparison of microarray platforms (cDNA vs
  Affymetrix)
• hybridization of individual samples vs pools
• variability of cell lines
• robustness of commonly used statistical methods

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Data available (so far)

• Hybridizations from Affymetrix HGU133 Chips
• Summary measure of intensities MAS5 (Affymetrix,
  2002)
• most commonly used, but other possibilities
  available
• Robust Multichip Analysis (Irizarry et al., 2002)
• Model-Based Expression Index (Li and Wong, 2001)
  (at least 16 chips!)

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Brief summary of data

• HGU133A
• chipA 22.283 probe sets
• chipB 22.645 probe sets
• Present
• chipA 48.5
• chipB 38.2
• pmltmm
• chipA 27
• chipB 31

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Methods for exploring reproducibility among
arrays

• Pearsons coefficient of correlation (common, but
  wrong!)
• Coefficient of variation
• Distribution of differences of intensities
• Altman and Blands plot (MA plot)

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Class comparison

• Identification of differentially expressed genes
  between treated and not treated cell lines
• t-tests (adjusting for multiple comparisons)
• all arrays
• only pooled arrays
• only individual arrays
• ANOVA (linear) model
• estimation of treatment effect, adjusting for
  pool effect and week effect

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Some results...

• Pooled variance t-test on whole data
• treated versus controls
• chipA
• 1948 plt0.001 (356 plt0.001 and abs(FC)gt2)
• 240 with Bonferroni correction
• chipB
• 743 plt0.001 (143 plt0.001 and abs(FC)gt2)
• 76 with Bonferroni correction

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Some results...

• Pooled variance t-test on pooled data
• treated versus controls
• chipA
• 204 plt0.001
• 189/(204, 1948) common to overall analysis
• 82 plt0.001 and abs(FC)gt2
• 82/(82, 356) common to overall analysis
• chipB
• 80 plt0.001
• 69/(80, 743) common to overall analysis
• 37 plt0.001 and abs(FC)gt2
• 37/(37, 143) common to overall analysis

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Some results...

• Pooled variance t-test on individual data
• treated versus controls
• chipA
• 669 plt0.001
• 594/(669, 1948) common to overall analysis
• 226 plt0.001 and abs(FC)gt2
• 221/(226, 356) common to overall analysis
• chipB
• 245 plt0.001
• 196(245, 743) common to overall analysis
• 80 plt0.001 and abs(FC)gt2
• 77/(80, 143) common to overall analysis

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Some results...

• Pooled variance ANOVA results
• treated versus controls
• chipA
• 1913 plt0.001
• 1624/(1913, 1948) common to overall analysis
• 343 plt0.001 and abs(FC)gt2
• 339/(343, 356) common to overall analysis
• chipB
• 245 plt0.001
• 196(245, 743) common to overall analysis
• 80 plt0.001 and abs(FC)gt2
• 77/(80, 143) common to overall analysis

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Conclusions

• ?????????
• So far no evidence for the usefulness of pooling
  data from cell lines no evidence of decreased
  variability
• but need to further investigate the differences
  in the individual versus pooled results
• Need for a plan of biological (quantitative)
  validations of expression measures