A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

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A robust neural networks approach for spatial and
intensity-dependent normalization of cDNA
microarray data

• A.L. Tarca, J.E.K. Cooke and J. MacKay

Presented by Dana Mohamed
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Microarrays
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Importance of Microarrays (and that the
data is correct)

• Assumption that microarray data linearly reflects
  amount of mRNA present in cell
• In turn, reflects gene expression levels
• If the data is incorrect,
• So is our interpretation of gene expression
• And therefore all the science built on that
  interpretation is also incorrect

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Where error is

• Intensity of Fluorescence
• Overall imbalance of dye intensity
• 2 dyes Cy5 (R) and Cy3 (G)
• If R G expressed at equal levels, R/G 1
• Space
• Intensities variable on coordinates
• Can be dirty on sides of microarray

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Previous Methods

• Many address intensity bias
• Few address spatial bias
• Most rely on M M m
• M is the normalized values
• M is the raw log-ratio (M log2R/G)
• m is the estimate of the bias

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Important Variables

• M log2(R/G)
• Log ratio converts multiplicative error to
  additive error
• A (1/2)0.5log2RG
• Average of the log-intensities
• Minus-add plots
• M vs. A
• Useful for assessing systematic bias

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Calculating m in other methods

• gMed global median normalization
• m median(Mi)
• Mi are all the values of M
• pLo print tip loess
• m ci (A)
• pLoGS
• found in GeneSight biodiscovery.com
• Local group median (3x3 square regions)
• print tip loess
• cPLo2D - print tip loess pure 2D normalization
• BioConductor bioconductor.org
• m a ci (A) ß ci (SpotRow,SpotCol)
• ci (SpotRow,SpotCol) is the loess estimate of M
  using spot row and column coordinates inside the
  ith print tip
• gLoMedF
• global loess normalization
• spatial median filter

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Robust Neural Networks Technique

• pNN2DA print tip robust neural nets 2D and A
• Attempt to find the best fit of M using A and the
  2-D space coordinates of the spots
• m ci (A,X,Y)
• Instead of using individual print tips use 3x3
  bins of them X and Y
• Accounts for spatial bias

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Neural Nets Terminology

• Uses multi-layer feedforward network
• Sigmoid Function

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Neural Networks

• Uses multi-layer feedforward network
• x is the vector (X,Y,A,1),
• I 3,
• w are the weights,
• sigma one represents the hidden neurons and they
  are sigmoid functions,
• sigma two is the single neuron in the output
  layer, which is also sigmoid,
• Sigma one J1 accounts for the second layer bias,
  
• J represents the number of neurons in the hidden
  layer of the network

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Multi-layered Feedforward
Usually, J 3 to take care of outliers but also
so as to avoid over-fitting
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Criteria Datasets

• Criteria
• a) reduce variability of log-ratios between
  replicated slides and within slides
• b) ability to distinguish truly regulated
  genes from the other genes
• Datasets
• Apo AI a,b
• Swirl Zebra Fish a
• Poplar experiment a
• Perturbed Apo AI b

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Classic Neural Nets vs. Robust NNets
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Criteria refresher

• The ability to reduce the variability of
  log-ratios between replicated slides and within
  slides
• The ability to distinguish truly regulated genes
  from the other genes

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Impact on Variability
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Cont. 3 Data Sets
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Downregulated Gene Sorting Apo AI set
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DRGS Perturbed Apo AI set
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Spatial Uniformity of M values distribution
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Results Table
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Strengths/Weaknesses

• Seems promising
• Uses multiple tests to determine efficacy
• Doesnt use enough datasets
• Uses patterned perturbed dataset
• But no real perturbed dataset

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Future Work

• More datasets
• When should this normalization technique be used
  over other techniques?
• Should this technique be combined with elements
  of other techniques to further improve it?

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References

• Tarca, A.L., J.E.K. Cooke, and J. Mackay. A
  robust neural networks approach for spatial and
  intensity-dependent normalization of cDNA
  microarray data." Bioinformatics Jun 2005 21
  2674 - 2683
• Haykin, Simon. Neural Networks A Comprehensive
  Foundation. New Jersey Prentice Hall, 1999.
• Mount, David W. Bioinformatics sequence and
  genome analysis. New York Cold Spring Harbor
  Laboratory Press, 2001.