Introduction%20to%20Microarrays

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Introduction to Microarrays

• Kellie J. Archer, Ph.D.
• Assistant Professor
• Department of Biostatistics
• kjarcher_at_vcu.edu

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Microarrays
A snapshot that captures the activity pattern of
thousands of genes at once.  
Affymetrix GeneChip
Custom spotted arrays
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Spotted Microarray Process
CTRL
TEST
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Affymetrix GeneChip Probe Arrays
Hybridized Probe Cell
GeneChip Probe Array
Single stranded, fluorescently labeled DNA target
24µm
Oligonucleotide probe
1.28cm
Each probe cell or feature contains millions of
copies of a specific oligonucleotide probe
Over 250,000 different probes complementary to
genetic information of interest
Image of Hybridized Probe Array
BGT108_DukeUniv
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Applications of microarrays

• Cancer research Molecular characterization of
  tumors on a genomic scale more reliable
  diagnosis and effective treatment of cancer
• Immunology Study of host genomic responses to
  bacterial infections
• Model organisms Multifactorial experiments
  monitoring expression response to different
  treatments and doses, over time or in different
  cell types
• etc.

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Applications of Microarrays

• Compare mRNA transcript levels in different type
  of cells, i.e., vary
• Tissue (liver vs. brain)
• Treatment (Drugs A, B, and C)
• State (tumor vs. normal)
• Organism (yeast, different strains)
• Timepoint
• etc.

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Affymetrix Design
11 20 Probe Pairs interrogate each gene
PM
GCGCCGGCTGCAGGAGCAGGAGGAG
GCGCCGGCTGCACGAGCAGGAGGAG
MM
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Image Analysis Pixel Level Data
6 x 6 matrix of pixels for each PM and MM
probe HG-U133A GeneChip
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Expression Quantification
PM and MM intensities are combined to form an
expression measure for the probe set (gene)
PM
GCGCCGGCTGCAGGAGCAGGAGGAG
GCGCCGGCTGCACGAGCAGGAGGAG
MM
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Expression Quantification

• Initially, Affymetrix signal was calculated as
  
• 
  where j indexes
  the probe pairs for each probe set A. This is
  known as the Average Difference method.
• Problems
• Large variability in PM-MM
• MM probes may be measuring signal for another
  gene/EST
• PM-MM calculations are sometimes negative

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Expression Quantification

• The mean of a random variable X is a measure of
  central location of the density of X.
• The variance of a random variable is a measure of
  spread or dispersion of the density of X.
• Var(X)E(X-?)2 E(X2) - ?2
• Standard deviation ?

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Expression QuantificationIllustration Average
Difference.xls
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Sources of Obscuring Variation in Microarray
Measurements

• Sample handling (degree of physical manipulation,
  time from extripation to freezing)
• Microarray manufacture
• Sample processing (extraction procedure, RNA
  integrity purity, RNA labeling)
• Processing differences (hybridization chambers,
  washing modules, scanners)
• Personnel differences
• Random differences in signal intensity in a data
  set which co vary with the biological process

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Normalization

• The purpose of normalization is to remove
  experimental artifacts of no direct interest,
  that is, the removal of systematic effects other
  than differential expression. Normalization
  procedures often include
• background subtraction,
• detection of outliers,
• and removal of variation due to
• differences in sample preparation,
• array differences,
• differences in dye labeling efficiencies,
• and scanning differences.

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16 Replicate HG-133A GeneChips, Before
normalization
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16 Replicate HG-133A GeneChips, After
normalization
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Taxonomy of Microarray Data Analysis Methods

• Unsupervised Learning The statistical analysis
  seeks to find structure in the data without
  knowledge of class labels.
• Supervised Learning Class or group labels are
  known a priori and the goal of the statistical
  analysis pertains to identifying differentially
  expressed genes (AKA feature selection) or
  identifying combinations of genes that are
  predictive of class or group membership.

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Unsupervised Learning

• Unsupervised learning or clustering involves the
  aggregation of samples into groups based on
  similarity of their respective expression
  patterns without knowledge of class labels.
• Examples of Unsupervised Learning methods include
• Hierarchical clustering
• k-means
• k-medoids
• Self Organizing Maps
• Principal Components
• Multidimensional Scaling

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Supervised Learning

• Example methods for Class comparison/ Feature
  selection include
• T-test / Wilcoxon rank sum test
• F-test / Kruskal Wallis test
• etc.
• Example methods for Class Prediction include
• Weighted voting
• K nearest neighbors
• Compound Covariate Predictors
• Classification trees
• Support vector machines
• etc.

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Supervised Learning Class Prediction

• Risk of over-fitting the data may have a perfect
  discriminator for the data set at hand but the
  same model may perform poorly on independent data
  sets.
• Most prediction methods are intended for large
  n (samples) small p (covariates) datasets.
• Process is to
• Fit model
• Check model adequacy
• Make an inference

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Class Prediction Checking model Adequacy

• Regardless of algorithm used, it is essential
  that once the prediction rule has been defined,
  an unbiased estimate of the true error rate must
  be calculated.

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Class Prediction Checking Model Adequacy

• In a data rich situation,
• randomly divide the dataset into two parts,
  representing a training and test dataset.
• Build the prediction algorithm using the training
  dataset
• Once a final model has been developed, the
  prediction rule is applied to the test dataset to
  estimate the misclassification error

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Class Prediction Checking Model Adequacy

• For small sample sizes, withholding a large
  portion of the data for validation purposes may
  limit the ability of developing a prediction
  rule. Therefore, use cross-validation techniques
  to assess the error.

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Class Prediction Checking Model Adequacy

• K-fold cross-validation requires one to randomly
  split the dataset into K equally sized groups.
• Thereafter, the model is fit to K-1 parts of the
  data and the generalization error is calculated
  using the Kth remaining part of the data.
• This procedure is repeated so that the
  generalization error is estimated for each of the
  K parts of the data, providing an overall
  estimate of the generalization error and its
  associated standard error.

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Class Prediction Checking Model Adequacy
1 2 3 4 5 6 7 8 9 10

• Leave out data in group 3
• Fit the model to the data in groups 1 2, 4
  10 (learning
• dataset)
• Calculate the error using observations in group
  3 as the
• test dataset
• Do this for each of the 10 partitions