Analysis of Gene Expression Anne R. Haake Rhys Price Jones

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Analysis of Gene ExpressionAnne R.
HaakeRhys Price Jones
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Gene Expression Analysis

• Connecting structural genomics to functional
  genomics

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How do we relate gene identity to cell
physiology, disease drug discovery?

• Functional Genomics
• development and application of global
  (genome-wide or system-wide) experimental
  approaches to assess gene function by making use
  of the information and reagents provided by
  structural genomics

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High Throughput Systems for Studying Global Gene
Expression are Complex

• Need to consider
• the biology behind the experiments the
  interpretation of the experiments
• advancements in biotechnology
• the computing issues

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The Flow of Information

• A gene is expressed in 2 steps
• DNA is transcribed into RNA (mRNA)
• RNA is translated into protein

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Genotype to Phenotype

• Individual cells in an organism have the same
  genes (DNA)
• the genotype
• but.not all genes are active (expressed) in
  each cell
• It is the expression of thousands of genes and
  their products (RNA, proteins), functioning in a
  complicated and orchestrated way, that make a
  specific cell what it is.
• the phenotype

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The Flow of Information
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Gene Expression Depends on Context

• The subsets of genes that are expressed
  (RNA/protein) will differ among cells, tissues,
  organs, conditions
• the subset expressed confers unique properties to
  the cell

neuron
liver
muscle
muscle
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Differential Gene Expression

• The level of expression of genes also differs
  with the cellular context
• i.e. the amount of a given RNA will vary
• We can think of gene expression in eukaryotes as
  having both an on/off switch and volume
  control

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Specific Patterns of Gene Expression

• Tissue/Cell type-specific
• -e.g. skin cell vs. brain cell
• -e.g. keratinocyte vs. melanocyte
• Developmental stage
• -e.g. embryonic skin cell vs. adult skin cell
• Disease state
• -e.g. normal skin cell vs. skin tumor cell
• Environment-specific (drugs, toxins)
• -e.g. skin cell untreated vs. treated

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Analyze Gene Expression

• We measure gene expression by analyzing the
  genetic molecule, messenger RNA (mRNA)
• We also often are interested in measuring
  proteins

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We Can Analyze RNA Content

• First, isolate mRNA from cells or tissues

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• Next, identify RNAs in the sample
• One to few RNAs at a time
• Multiple RNAs (high throughput techniques)
• Sometimes called global expression analysis
• Identify by hybridization (base-pairing)

• radioactive or enzyme-linked probe to the
  immobilized RNA
• Probe is complementary to RNA of interest
• Called cDNA

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RNA Expression Analysis

• One type of analysis is called a Dot Blot

samples are spotted onto filter and then
hybridized with labeled probe
So, the sequence is used to generate the data
(via hybridization) but the data itself is image
data. We scan the images to get intensities for
each spot.
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State of the Art High-Throughput Methods

• Multiple genes? entire genome expression analyzed
  at once!
• RNA (the transcriptome)
• DNA microarrays
• SAGEserial analysis of gene expression
• MPSS multiple parallel signature sequencing
• Proteins (the proteome)
• protein arrays
• mass spectrophotometry

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Gene Expression Analysis

• Thousands of different mRNAs are present in a
  given cell together they make up the
  transcriptional profile
• It is important to remember that when a gene
  expression profile is analyzed in a given sample,
  it is just a snapshot in time and space.

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Regulation of Gene ExpressionHow Do Different
Transcriptional Profiles Arise?

• Prokaryotes (e.g. bacteria)
• simple organisms gene expression responsive
  primarily to environment
• sets of genes are generally on or off
• functionally related genes are organized into
  units called Operons
• Eukaryotes
• Not just on/off and volume control but even more
  complicated!
• complex multi-level control

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The Expression Snapshot
All of these mechanisms together determine which
RNAs/proteins are present in the snapshot.
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Gene Regulatory Networks

• Genes act in concert
• Interrelationships are complex!
• Scientists rarely get funded to study one gene
  anymore!!
• We need ways to understand what the snapshot
  means

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Gene Networks

• "The approach to biology for the past 30 years
  has been to study individual proteins and genes
  in isolation. The future will be the study of
  the genes and proteins of organisms in the
  context of their informational pathways or
  networks."
• Leroy Hood, Director of the Institute for Systems
  Biology, Nature, Oct. 19, 2000.

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Gene Networks Some Examples

• Genes and their products are related through
  their roles in
• metabolic pathways
• cell signalling networks

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Metabolic Pathway
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Cell Signalling Networks
www.mpi-dortmund.mpg.de/departments/dep1/signaltra
nsduktion/image3.gif
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What can we learn by studying global patterns of
gene expression?

• Individual gene expression patterns
• Classifications for diagnosis, prediction
• Groups of Genes
• Molecular taxonomy of disease
• Gene Networks/Pathways
• Reconstruction of metabolic regulatory pathways

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Gene Expression Analysis

• Biotechnology
• High-Throughput techniques of Biochemistry/
  Molecular Biology
• RNA or protein

• Informatics
• Management of large, complex data sets
• Data mining to gain useful information

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High Throughput Techniques

• We will only consider microarrays in detail today

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Gene Expression Analysis Using Microarrays
Experimental Design Array Production Sample
Preparation Scanning Image Analysis Data
Processing Data Analysis Information Integration
Biology Wet Lab
Computer Workstation
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GeneChip vs Spotted Arrays

• GeneChip Arrays use oligonucleotides
• Oligos arrays are built on a solid support

• Spotted arrays utilize nucleic acids made in
  solution
• Solutions are then spotted onto a solid support

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What are cDNA (Spotted) Microarrays?

• A miniaturized simultaneous version of the
  traditional cDNA
• dot blot
• Enables massive gene expression profiling
• 10,000s genes at once
• cDNA probes are amplified directly from
  culture
• by PCR and purified
• Purified probes are printed on to coated glass
  slides

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cDNA Microarray Expression Analysis
Duggan et al. (1999) Nature Genetics 21 11
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ScanAnalyze Image Analysis

1. Spot Finding
2. Background subtraction
3. Intensity Calculation

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Image Analysis Software

• Freeware or Shareware
• ScanAlyze
• MAGIC (MicroArray Genome Imaging and Clustering
  Tool)
• Spot
• Automated spot finding

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What do the spots represent?

• Fluorescence intensity is a measure of the
  relative abundance of individual mRNAs
• Experimental relative to control
• expressed as a ratio from spotted arrays
  (2-color)
• http//www.ncbi.nlm.nih.gov/geo/query/acc.cgi?ac
  cGSM1801
• Have to be careful when comparing between arrays
  from experiment to experiment.

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GeneChip Oligonucleotide Array
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GeneChip Expression AnalysisHybridization and
Staining
Array
Hybridized Array
cRNA Target
Streptavidin-phycoerythrin conjugate
Courtesy of M. Hessner, CAAGED Workshop
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Affymetrix Chips
300,000 Probes Perfect Match and
Mismatch Average Difference Values Courtesy of J.
Glasner CAAGED Workshop
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Current Problems Facing Expression Analysis on
the Biotech side

• Standardization quality control in the
  experiments (data quality at many levels)
• Cost

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Problem in reproducibility of experimental data

• Lots of variation in arrays
• more than 100 experimental steps
• Sources of variation
• biological variability in each RNA extract
• each labeling reaction is different
• each slide is a separate hybridization
• spots on the slide are variable across slides
  (and within slides when double spotted)
• each color is scanned separately
• Need Replicates and Statistics!

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The Value of Replicates

• What is a replicate?
• Doing the same experiment more than once
• An experimental design issue
• How many?
• Most people dont do true replicates
• Why not?
• cost is primary
• limiting sample size
• ignorance of statistical considerations
• competition

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Outcome

• Noisy data
• Data preprocessing is necessary
• normalization
• scaling
• Heavy reliance on statistics today

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Pre-processing

• Gene filtering
• control genes
• uninformative genes
• Normalization and scaling
• allows comparisons across arrays
• scaling to control dynamic range
• Transformation
• logarithmic transformation for improved
  statistical properties

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Normalization
Cy5 signal (log2)
Cy3 signal (log2)
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Outcomes of Microarray Analysis

• Large, complex data sets
• example of a routine study
• 50,000 genes from 20 samples -? approx. 1-2
  X 106 pieces of data
• challenges for Bioinformatics
• annotation, storage, retrieval, sharing of data
• information from the data

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Current State of Microarray Data Availability

• Wide availability of technology has given rise to
  a large number of distributed databases
• data scattered among many independent sites
  (accessible via Internet) or not publicly
  available at all
• Need for standardization!

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Public Repositories Efforts Towards
Standardization

• GeneX at US National Center for Genome Resources
• ArrayExpress at European Bioinformatics Institute
  
• Gene Expression Omnibus at US National Center for
  Biotechnology Information
• Stanford University Database

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Standardization of the biological databases is a
big issue

• A prime example of databases in need of
  standardization the gene expression databases
• Why?
• wide availability of technologies such as the
  microarray has given rise to a large number of
  heterogeneous, distributed databases
• differ in annotation, database structure,
  availability
• standardization is necessary to enable scientists
  to share and compare data

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MGED Group and Standardization Issues

• Microarray Gene Expression Database (MGED) Group
  
• www.mged.org
• MGED is taking on the challenge of
  standardization
• Four major projects

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MGED Projects

• MIAME - The formulation of the minimum
  information about a microarray experiment
  required to interpret and verify the results.
• MAGE - The establishment of a data exchange
  format (MAGE-ML) and object model (MAGE-OM) for
  microarray experiments.

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MGED Projects

• Ontologies - The development of ontologies for
  microarray experiment description and biological
  material (biomaterial) annotation in particular.
• Normalization - The development of
  recommendations regarding experimental controls
  and data normalization methods.

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Some Basic Statistics

• dot product
• mean
• standard deviation
• log base 2
• etc.
• util.ss

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Gene chips

• Spots representing thousands of genes
• Two populations of cDNA
• different conditions to be compared
• One colored with Cy5 (red)
• One colored with Cy3 (green)
• Mixed, incubated with the chip
• Figures from Campbell-Heyer Chapter 4

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Red/Green Intensity measurements

• (define redgreens '((2345 2467) (3589 2158)
  (4109 1469) (1500 3589) (1246 1258) (1937 2104)
  (2561 1562) (2962 3012) (3585 1209) (2796 1005)
  (2170 4245) (1896 2996) (1023 3354) (1698 2896)))
• Shows (red green) intensities for 14 (out of
  6200!) genes

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Should we normalize?

• Average of reds is 2386.9
• Average of greens is 2380.3
• What does John Quackenbush say? (page 420)
• Calculate standard deviations.
• Return to this issue
• For now, no normalization

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Ratios of red values to green

• (define redgreenratios (map (lambda (x)
  (round2 (/ (car x) (cadr x)))) redgreens))
• Produces (0.95 1.66 2.8 0.42 0.99 0.92 1.64 0.98
  2.97 2.78 0.51 0.63 0.31 0.59)
• Which genes are expressed more in red than green?
• Should these values be normalized?

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Yet another Color scheme

• (0.95 1.66 2.8 0.42 0.99 0.92 1.64 0.98 2.97 2.78
  0.51 0.63 0.31 0.59)
• Highly expressed Neutral Less expressed
• gt2.0 gt1.3 close to 1.0 gt0.5 lt0.5
• Seems arbitrary?
• Log scale??
• Why oh why did they re-use red and green?
• Clustering? Meaning?

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Larger experiment

• 12 Genes
• Expression values at 0, 2, 4, 6, 8 and 10 hours

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Table 4.2 of Campbell/Heyer

• Name 0 hrs 2 hrs 4 hrs 6 hrs 8 hrs 10
  hrsC 1 8 12 16 12
  8 D 1 3 4 4 3
  2 E 1 4 8 8 8
  8 F 1 1 1 .25
  .25 .1 G 1 2 3 4
  3 2 H 1 .5 .33 .25
  .33 .5 I 1 4 8 4
  1 .5 J 1 2 1 2
  1 2 K 1 1 1
  1 3 3 L 1 2 3
  4 3 2 M 1 .33
  .25 .25 .33 .5 N 1 .125
  .0833 .0625 .0833 .125
• Normalized how?

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Take logs

• C 0 3.0 3.58 4.0 3.58 3.0
• D 0 1.58 2.0 2.0 1.58 1.0
• E 0 2.0 3.0 3.0 3.0 3.0
• F 0 0 0 -2.0 -2.0 -3.32
• G 0 1.0 1.58 2.0 1.58 1.0
• H 0 -1.0 -1.6 -2.0 -1.6 -1.0
• I 0 2.0 3.0 2.0 0 -1.0
• J 0 1.0 0 1.0 0 1.0
• K 0 0 0 0 1.58 1.58
• L 0 1.0 1.58 2.0 1.58 1.0
• M 0 -1.6 -2.0 -2.0 -1.6 -1.0
• N 0 -3.0 -3.59 -4.0 -3.59 -3.0
• Compare

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How Similar are two Rows?

• How similar are the expressions of two genes?
• First well normalize each row
• (define normalize substract mean and divide
  by sd
• (lambda (l)
• (let ((m (mean l))
• (s (standarddeviation l)))
• (map (lambda (x) (/ (- x m) s)) l))))
• What are the new mean and standard deviation?

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How Similar are two Rows?

• Calculate the Pearson Correlation between pairs
  of rows
• (define pc pearson correlation
• (lambda (xs ys)
• (/ (dotproduct (normalize xs) (normalize ys))
  (length xs))))
• gt (pc '( 1 2 3 4 3 2 ) row G
• '( 1 2 3 4 3 2 )) row L
• 1.0
• gt (pc '( 1 2 3 4 3 2 ) row G
• '( 1 3 4 4 3 2 )) row D
• 0.8971499589146109

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Some other pairs

• Name 0 hrs 2 hrs 4 hrs 6 hrs 8 hrs 10
  hrsC 1 8 12 16 12
  8 D 1 3 4 4 3
  2 E 1 4 8 8 8
  8 F 1 1 1 .25
  .25 .1 G 1 2 3 4
  3 2 H 1 .5 .33 .25
  .33 .5 I 1 4 8 4
  1 .5 J 1 2 1 2
  1 2 K 1 1 1
  1 3 3 L 1 2 3
  4 3 2 M 1 .33
  .25 .25 .33 .5 N 1 .125
  .0833 .0625 .0833 .125
• gt (pc '( 1 3 4 4 3 2) row D
• '( 1 .33 .25 .25 .33 .5)) row M
• -0.9260278787295065
• gt (pc '( 1 2 3 4 3 2) row G
• '( 1 .5 .33 .25 .33 .5)) row H
• -0.9090853650855358

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Correlation is sensitive to relative magnitudes

• pc(G,L) 1 -- identically expressed genes
• pc(G,D) .897 -- similarly expressed genes
• pc(D,M) -.926 -- reciprocally expressed
• pc(G,H) -.909 -- also reciprocally expressed
• What happens if, instead of using the expression
  data we use the log transforms?
• pc(G,L) 1.0
• pc(G,D) 0.939
• pc(D,M) -1.0
• pc(G,H) -1.0

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Hierarchical Clustering

• Repeat
• Replace the two closest objects by their
  combination
• Until only one object remains

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What are the objects?

• (define objects
• (map (lambda (x)
• (cons
• (symbol-gtstring (car x))
• (cdr x)))
• logtable42))
• Initially, the objects are the genes with the log
  transformed expression levels
• Typical object
• ("E" 0 2.0 3.0 3.0 3.0 3.0)

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Combining objects

• (define combine
• (lambda (xs ys)
• (cons (string-append (car xs) (car ys))
• (map (lambda (x y) (/ ( x y) 2.0))
• (cdr xs) (cdr ys)))))
• combine names
• average the entries
• Typical combined pair
• ("EG" 0 1.5 2.29 2.5 2.29 2.0))

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Manual Hierarchical Clustering

• Lets go to emacs

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K-means Clustering -- Lloyds Algorithm

• Partition data into k clusters
• REPEAT
• FOR each datapoint
• Calculate its distance to the centroid of each
  cluster
• IF this is minimal for its own cluster
• Leave the datapoint in its current cluster
• ELSE
• Place it in its closest cluster
• UNTIL no datapoint is moved
• Goal minimize sum of distances from datapoints
  to centroids

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Analysis of k-means clustering

• There are always exactly k clusters
• No cluster is empty (why?)
• The clusters are not hierarchical
• The clusters do not overlap
• Run time with n datapoints
• Partitioning O(n)
• FOR loop is O(nk)
• REPEAT loop is ???
• kanungo et al

Partition data into k clusters REPEAT FOR each
datapoint Calculate its distance to the
centroid of each cluster IF this is minimal for
its own cluster Leave the datapoint in its
current cluster ELSE Place it in its closest
cluster UNTIL no datapoint is moved
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Pro and Con

• Pro
• With small k, may be faster than hierarchical
• Clusters may be tighter
• Con
• Sensitive to initial choice of k
• Sensitive to initial partition
• May converge to local, rather than global minimum
• Not clear how good resulting clusters are

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Other Methods for Clustering

• Self Organizing Maps
• SWARM technology
• SOM/SWARM hybrid

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Mining of Expression Data

• A gene expression pattern derived from a single
  microarray is simply a snapshot (one
  experimental sample vs reference)
• Usually want to understand a process or changes
  in expression over a collection of samples
• ? gene expression profile

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Three levels of microarray gene expression data
processing
Brazma et al., Nature Genetics, 29365-371, 2001
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Goal of Analysis of Expression Matrix

• Some statistical methods applied to
• Group similar genes together gt groups of
  functionally similar genes.
• Group similar cell samples together.
• Extract representative genes in each group.

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Typical approach

• Look for patterns
• compare rows to find evidence for co-regulation
  of genes
• compare columns to find evidence for relatedness
  among samples
• 1) Choose a measure of similarity (distance)
  among the objects being compared-each row or
  column is considered a vector in space
• 2) Then, group together objects (genes or
  samples) with similar properties-is a
  multidimensional analysis

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Pattern Recognition

• Clustering
• Feature extraction/selection
• Classification-discrimination analysis

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Analytic Approaches

• Clustering Identification of associations
  between data points organization of data into
  groupsexploratory analysis
• Clustering Algorithms
• Hierarchical
• K-means
• Self-organizing maps
• Others

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samples
g e n e s
Gene Expression Matrix Hierarchical Clustering
Eisen et al. http//www.pnas.org/cgi/content/full
/95/25/14863
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Feature Selection Classification

• First, identify features (genes) that
  discriminate between classes
• Then use features for classification
• machine learning approach
• supervised analysis
• assignment of a new samplepatternto a
  previously specified class, based on sample
  features and a trained classifier

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Classic Example Classification of AML vs. ALL

• Biological/Clinical Problems
• previously, no single reliable test to
  distinguish them
• differ greatly in clinical course response to
  treatments

• Comparing 2 acute leukemias
• acute myeloid leukemia (AML)
• acute lymphoid leukemia (ALL)

Golub et al., Science Oct 15 1999 531-537
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Study Design
Golub et al., Science Oct 15 1999 531-537
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Results of the study

• 1) Clustering of microarray data using tumors of
  known type
• ? found 1100 of 6817 genes correlated with class
  distinction
• 2) Formation of a class predictor 50 most
  informative genes used as a training set
• ? classification of unknown tumors

Golub et al., Science Oct 15 1999 531-537
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The prediction of a new sample is based on
'weighted votes' of a set of informative genes
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Free Software for Microarray Analysis

• Cluster TreeView
• Michael Eisen
• http//rana.lbl.gov/EisenSoftware.htm

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More Free Software

• Expression Profiler (European Bioinformatics
  Inst)
• http//ep.ebi.ac.uk/
• GeneX (National Center for Genome Research)
• http//www.ncgr.org/genex/index.html
• ArrayViewer and MEV (TIGR)
• http//www.tigr.org/software/
• Many More!!

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Microarray Analysis Software

• Popular commercial packages
• Spotfire DecisionSite (Spotfire, Inc)
• GeneSpring (Silicon Genetics)
• Affymetrix Microarray Suite
• Affymetrix Data Mining Tool (Affymetrix, Inc.)
• Rosetta Inpharmatics' Resolver

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Why cluster analysis may not be the answer

• Clustering methods typically require user inputs
• Example distance measure
• Clustering methods differ in the way that the
  number of clusters are specified.
• Clustering methods are often sensitive to the
  initialization condition (starting guess)
• Local vs. global sampling of clustering space

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Cluster Analysis Challenges

• Noise in the data itself
• Large data sets
• most of the techniques currently used were not
  developed for multidimensional data
• What about networks?
• limitation of cluster analysis similarity in
  expression pattern suggests co-regulation but
  doesnt reveal cause-effect relationships

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Using information networks as an interpretive
layer between phenotypes and the underlying
genes, proteins and metabolites
Highly connected genes are often critical in the
onset of cancer and metabolic diseases. However,
drug treatment targeting less connected genes
will have fewer side effects.
J. Blanchard-CAAGED Workshop 2002
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Other Analytic Approaches are Being Explored to
Reverse Engineer Networks

• Bayesian Networks
• represent the dependence structure between
  multiple interacting quantities (e.g. expression
  levels of genes)
• gene interactions models of causal influence
• good for noisy data

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Analytic Challenges

• Advanced methods may require significant
  computational resources
• Numerically complex calculations (large
  correlation matrices)
• Combinatorically large search spaces (Bayesian
  nets)
• Many training cycles (neural nets)
• Global optimization (genetic algorithms)
• Archiving, indexing, and correlation of large
  datasets (data extraction and data mining
  visualization)

S. Atlas CAAGED Workshop
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Take-home Messages

• With current technology global gene expression
  studies are best used for hypothesis building
• Other experimental methods and smaller data sets
  needed to address the reproducibility problem
• New analytical approaches are needed to deal with
  the multidimensional data
• Need for high performance computing
• Need for Bio/CS/IT/Stats people to work together!