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Cross-site and Cross-platform Concordance of Microarray Analysis Improved by Variance Stabilization. Pan Du, Simon Lin Robert H. Lurie Comprehensive Cancer Center. Outline. Why Variance Stabilization? How to Stabilize Variance? Illumina Affymetrix Does it work?.

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Cross-site and Cross-platform Concordance of Microarray Analysis Improved by Variance Stabilization


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cross site and cross platform concordance of microarray analysis improved by variance stabilization

Cross-site and Cross-platform Concordance of Microarray Analysis Improved by Variance Stabilization

Pan Du, Simon Lin

Robert H. Lurie Comprehensive Cancer Center

outline
Outline
  • Why Variance Stabilization?
  • How to Stabilize Variance?
    • Illumina
    • Affymetrix
  • Does it work?
introduction of microarray studies
Introduction of Microarray Studies

normal

cancer

A

A

Array x

Array y

Array x

Array y

Quality Control Studies

Biomedical Applications

(Johnson and Lin, Nature 411:885, 2001)

evaluation criterion of reproducibility concordance

Lab A

Lab B

Gene list B

Gene list A

Anything in common?

ideal

100

better

% in common

worse

number of genes selected

Evaluation criterion of reproducibility: Concordance
  • FDA-led Quality Control Study
    • cross-time
    • cross-site
    • cross-platform

(Tong et al., Nature Biotech 24:1132, 2006)

general microarray analysis procedure

Sample preparation

Microarray experiment and data collection

Background adjustment

Transformation

Normalization

Gene identification

General Microarray Analysis Procedure

(log2)

why variance stabilization
Why Variance Stabilization?

Ideal raw x log2 (x) log2 (x+offset)

x-y plot

mean-var plot

why do we care
Why do we care?
  • A general assumption of statistical tests to microarray data: variance is independent of intensity

Gene A: 7 (normal) → 8 (cancer)

Gene B: 13 (normal) → 14 (cancer)

variance stabilization the model
Variance Stabilization: the model
  • A mathematical model of microarray hybridization

(Rocke and Durbin, Bioinformatics 19:996, 2003)

variance stabilization deriving h y
Variance Stabilization: deriving h(y)
  • Asymptotic variance-stabilizing transformation can be achieved by

(Tibshirani, JASA, 1988)

huber s solution 2002
Huber’s Solution (2002)
  • VSN (Variance Stabilizing Normalization)
    • Estimate the mean and variance from a set of arrays
    • Assume most genes are not differentially expressed
    • Technically challenging because the normalization between arrays has to be considered
    • Practically challenging because usually we have only 2 ~ 6 arrays

(Huber et al., Bioinformatics, 2002)

illumina beadarray technology
Illumina BeadArray Technology

Larger than 30 technique replicates are on each array.

Beads are randomly assembled and held in these microwells

Multiple arrays on the same slide

Cost: < $200

variance stabilizing transformation vst
Variance Stabilizing Transformation (VST)

Fit the relations between mean and standard deviation

Relations between log2 and VST (arcsinh)

(Lin, Pan, Huber, and Warren, 2007)

evaluation data sets
Evaluation Data Sets
  • Barnes data: (Barnes, M., et al., 2005)
    • measured a dilution series (two replicates and six dilution ratios) of two human tissues: blood and placenta.
  • MAQC-I: (Shippy, R., et al., 2006)
    • Similar dilution series, conducted at more than one microarray facilities using both Illumina and Affymetrix platforms
cross site concordance evaluation
Cross-site concordance evaluation

MAQC data

VST improves the cross-site concordance

vst for affymetrix
VST for Affymetrix
  • Hypothesis: VST also works for Affymetrix arrays
    • Treat each pixel as a technical replicate
    • Model the mean and variance the same way
cross platform affymetrix and illumina
Cross-platform: Affymetrix and Illumina
  • Evaluation procedure
    • Comparing sample C and D in the MAQC study
    • The probe ids were first mapped to the Entrez IDs.
  • Legend notation
    • “Current”: RMA (affymetrix), Log2+Quantile (Illumina)
    • “Improved”: VST+RMA (affymetrix); VST+Quantile
bioconductor lumi package
Bioconductor lumi package
  • The VST and related algorithms are included in the Bioconduction lumi package
  • Bioconductor: http://www.bioconductor.org
acknowledgements
Acknowledgements
  • Robert H. Lurie Comprehensive Cancer Center, Northwestern University
    • Warren A. Kibbe and other members in the Bioinformatics group
    • Denise Scholtens, Biostatistics
  • European Bioinformatics Institute
    • Wolfgang Huber
  • The Walter and Eliza Hall Institute of Medical Research, Australia
    • Gordon Smyth