Analysis of Gene Expression Data

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Analysis of Gene Expression Data

• Yoonsoo Pyon
• ysp2_at_case.edu
• Feb. 8th, 2008

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MicroArray

• What are they?
• allow 1000s of expression analyses to be
  performed concurrently.

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DNA Chip Microarrays

• Put a large number (100K) of cDNA sequences or
  synthetic DNA oligomers onto a glass slide (or
  other subtrate) in known locations on a grid.
• Label an RNA sample and hybridize
• Measure amounts of RNA bound to each square in
  the grid
• Make comparisons
• Cancerous vs. normal tissue
• Treated vs. untreated
• Time course
• Many applications in both basic and clinical
  research

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Goals of a Microarray Experiment

• Find the genes that change expression between
  experimental and control samples
• Classify samples based on a gene expression
  profile
• Find patterns Groups of biologically related
  genes that change expression together across
  samples/treatments

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Potential Microarray Applications

• Drug discovery / toxicology studies
• Mutation/polymorphism detection Differing
  expression of genes over
• Time
• Tissues
• Disease States
• Sub-typing complex genetic diseases

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cDNA Microarray Technologies

• Spot cloned cDNAs onto a glass microscope slide
• usually PCR amplified segments of plasmids
• Label 2 RNA samples with 2 different colors of
  flourescent dye - control vs. experimental
• Mix two labeled RNAs and hybridize to the chip
• Make two scans - one for each color
• Combine the images to calculate ratios of amounts
  of each RNA that bind to each spot

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Spot your own Chip
Robot spotter
Ordinary glass microscope slide
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cDNA Spotted Microarrays
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Data Acquisition

• Scan the arrays
• Quantitate each spot
• Subtract background
• Normalize
• Export a table of fluorescent intensities for
  each gene in the array

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MicroArray

• Overview of image analysis
• Grid finding
• grid alignment
• skew
• Quantification of image
• variable background
• uneven hybridization

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Image Analysis/Data Quantization

• Feature (target ? probe) segmentation
• Data extraction and quantization of
• Background
• Feature
• Correlation of feature identity and location
  within image
• Display of pseudo-color image

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Image Segmentation

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Normalization

• Can control for many of the experimental sources
  of variability (systematic, not random or gene
  specific)
• Bring each image to the same average brightness
• Can use simple math or fancy -
• divide by the mean (whole chip or by sectors)
• LOESS (locally weighted regression)
• No sure biological standards

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Are the Treatments Different?

• Analysis of microarray data has tended to focus
  on making lists of genes that are up or down
  regulated between treatments
• Before making these lists, ask the
  question "Are the treatments different?"
• Use standard statistical methods to evaluate
  expression profiles for each treatment (t-test or
  f-test)
• If there are differences, find the genes most
  responsible
• If there are not significant overall differences,
  then lists of genes with large fold changes may
  only reflect random variability.

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Microarray Experiment Design

• Type I (n 2)
• How is this gene expressed in target 1 as
  compared to target 2?
• Which genes show up/down regulation between the
  two targets?
• Type II (n gt 2)
• How does the expression of gene A vary over time,
  tissues, or treatments?
• Do any of the expression profiles exhibit similar
  patterns of expression?

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Basic Data Analysis

• Fold change (relative increase or decrease in
  intensity for each gene)
• Set cutoff filter for low values (background
  noise)
• Cluster genes by similar changes - only really
  meaningful across multiple treatments or time
  points
• Cluster samples by similar gene expression
  profiles

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Streamlined Affy Analysis
Normalize
Filter
Present/AbsentMinimum valueFold change
Raw data
Classification
Significance
Clustering
Machine learning
t-test Rank Product
Gene lists
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Differential Expression

• Type I analysis
• Look for genes with vastly different expression
  under different conditions
• How do you measure vastly different?
• What role should derived statistics play?

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Type I Differential Expression
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Multiple Test

• In a microarray experiment, each gene (each probe
  or probe set) is really a separate experiment
• Yet if you treat each gene as an independent
  comparison, you will always find some with
  significant differences
• (the tails of a normal distribution)

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Multiple test

• Bonferroni correction
• ag/n global level divided by the number of
  tests
• Too strict
• Holms stepwise correction
• If p1 lt ag/n then adjust the remaing n-1 p-values
  by comparing the next p-value p2 lt ag/(n-1).
• If m is the largest integer for which pm lt
  ag/(n-m1), then we call gene 1, , m is
  significantly differentially expressed.
• Still too strict

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False Discovery

• Statisticians call false positives a "type 1
  error" or a "False Discovery"
• False Discovey Rate (FDR) is equal to the p-value
  of the t-test X the number of genes in the array
• For a p-value of 0.01 X 10,000 genes 100
  false different genes
• You cannot eliminate false positives, but by
  choosing a more stringent p-value, you can keep
  them manageable (try p0.001)
• The FDR must be smaller than the number of real
  differences that you find - which in turn depends
  on the size of the differences and varability of
  the measured expression values

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type I , II error
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Higher LevelMicroarray data analysis

• Clustering and pattern detection
• Data mining and visualization
• Controls and normalization of results
• Statistical validatation
• Linkage between gene expression data and gene
  sequence/function/metabolic pathways databases
• Discovery of common sequences in co-regulated
  genes
• Meta-studies using data from multiple experiments

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Clustering

• Identify co-regulated genes with microarray
  experiments (assumption?)
• Identify genes with similar expression
• Grouping unknown genes with known genes may
  provide insight into function of unknown genes
• Only useful for genes with varying expression
  levels

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Types of Clustering

• Herarchical
• Link similar genes, build up to a tree of all
• Self Organizing Maps (SOM)
• Split all genes into similar sub-groups
• Finds its own groups (machine learning)
• Principle Component Analysis (PCA)
• every gene is a dimension (vector), find a single
  dimension that best represents the differences in
  the data

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Clustering

• Pairwise similarity measure
• Minkowskys distance
• if q 1 ? Manhattan distance
• if q2 ? Euclidean distance
• Pearsons or Spearmans correlation coefficient
• Treating with missing value

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Clustering

• Data transformation
• Useful before compute pairwise similarity
• Ex) x1(100,200,300), x2(10,20,30),
  x3(30,20,10)
• Divide each component xj of p-dimensional data
  vector by its Euclidean norm

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Hierarchical Clustering
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Hierarchical Clustering

• require
• Dissimilarity measure between pair of cluster
• Update procedure for recalculation of merged
  cluster
• Weakness
• do not repair false joining of data points from
  previous step

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K-means clustering
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Self Organizing Maps
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Classification vs. Clustering

• Purpose
• Clustering To partition genes into
  co-expression group by suitable optimization
  method
• Classification To assign given condition to
  preexisting classes of condition

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Classification

• How to sort samples into two classes based on
  gene expression data
• Cancer vs. normal
• Cancer sub-types (benign vs. malignant)
• Responds well to drug vs. poor response (i.e.
  tamoxifen for breast cancer)

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Support Vector Machine (SVM)

• Main idea Select hyperplane that is more likely
  to generalize on a future datum

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Cross-validation

• Holdout validation
• K-fold cross-validation
• Leave-one-out cross-validation

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Reverse Engineering Genetic Networks

• Reconstruction of the interactions in a
  qualitative way from experimental data
• Once determined, these networks can be used to
  predict gene expression of corresponding genes
• Can we reconstruct the qualitative interactions
  of corresponding genes? No.
• Time-dependent measurement
• Knockout experiments

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Gene Regulatory Networks
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Network motif

• Problem Dimensionality of gene regulatory
  network
• Breakdown this network into small components
  called network motif and connect to Ensemble
  network