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![Page 1: 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.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d765503460f94a582fc/html5/thumbnails/1.jpg)
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
![Page 2: 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.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d765503460f94a582fc/html5/thumbnails/2.jpg)
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 = α ci (A) + β 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 J+1 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 FeedforwardUsually, J = 3 to take care of outliers but also so as to avoid over-fitting
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Criteria & DatasetsCriteria:
a) reduce variability of log-ratios between replicated slides and within slides
b) ability to distinguish truly regulated genes from the other genes
Datasets:
1) Apo AI: a,b
2) Swirl Zebra Fish: a
3) Poplar experiment: a
4) 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
• Doesn’t 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.