CZ5225: Modeling and Simulation in Biology Lecture 2: Gene Expression Profiles and Microarray Data Analysis Prof. Chen Yu Zong Tel: [phone] Email: [e-mail] http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, NUS

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CZ5225 Modeling and Simulation in Biology
Lecture 2 Gene Expression Profiles and
Microarray Data AnalysisProf. Chen Yu
ZongTel 6874-6877Email yzchen_at_cz3.nus.edu.sg
http//xin.cz3.nus.edu.sgRoom 07-24, level 7,
SOC1, NUS
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Biology and Cells

• All living organisms consist of cells.
• Humans have trillions of cells. Yeast - one
  cell.
• Cells are of many different types (blood, skin,
  nerve), but all arose from a single cell (the
  fertilized egg)
• Each cell contains a complete copy of the genome
  (the program for making the organism), encoded in
  DNA.

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DNA

• DNA molecules are long double-stranded chains 4
  types of bases are attached to the backbone
  adenine (A), guanine (G), cytosine (C), and
  thymine (T). A pairs with T, C with G.
• A gene is a segment of DNA that specifies how to
  make a protein.
• Human DNA has about 25-35K genes Rice about
  50-60K but shorter genes.

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Exons and Introns

• exons are coding DNA (translated into a protein),
  which are only about 2 of human genome
• introns are non-coding DNA, which provide
  structural integrity and regulatory (control)
  functions
• exons can be thought of program data, while
  introns provide the program logic
• Humans have much more control structure than rice

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Gene Expression

• Cells are different because of differential gene
  expression.
• About 40 of human genes are expressed at one
  time.
• Gene is expressed by transcribing DNA into
  single-stranded mRNA
• mRNA is later translated into a protein
• Microarrays measure the level of mRNA expression

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Molecular Biology Overview
Nucleus
Cell
Chromosome
Protein
Gene (DNA)
Gene (mRNA), single strand
cDNA
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Gene Expression

• Genes control cell behavior by controlling which
  proteins are made by a cell
• House keeping genes vs. cell/tissue specific
  genes
• Regulation
• Transcriptional (promoters and enhancers)
• Post Transcriptional (RNA splicing, stability,
  localization -small non coding RNAs)

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Gene Expression

• Regulation
• Translational (3UTR repressors, poly A tail)
• Post Transcriptional (RNA splicing, stability,
  localization -small non coding RNAs)
• Post Translational (Protein modification
  carbohydrates, lipids, phosphorylation,
  hydroxylation, methlylation, precursor protein)

cDNA
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Gene Expression Measurement

• mRNA expression represents dynamic aspects of
  cell
• mRNA expression can be measured with latest
  technology
• mRNA is isolated and labeled with fluorescent
  protein
• mRNA is hybridized to the target level of
  hybridization corresponds to light emission which
  is measured with a laser

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Traditional Methods

• Northern Blotting
• Single RNA isolated
• Probed with labeled cDNA
• RT-PCR
• Primers amplify specific cDNA transcripts

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Microarray Technology

• Microarray
• New Technology (first paper 1995)
• Allows study of thousands of genes at same time
• Glass slide of DNA molecules
• Molecule string of bases (25 bp 500 bp)
• uniquely identifies gene or unit to be studied

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Gene Expression Microarrays

• The main types of gene expression microarrays
• Short oligonucleotide arrays (Affymetrix)
• cDNA or spotted arrays (Brown/Botstein).
• Long oligonucleotide arrays (Agilent Inkjet)
• Fiber-optic arrays
• ...

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Fabrications of Microarrays

• Size of a microscope slide

Images http//www.affymetrix.com/
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Differing Conditions

• Ultimate Goal
• Understand expression level of genes under
  different conditions
• Helps to
• Determine genes involved in a disease
• Pathways to a disease
• Used as a screening tool

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Gene Conditions

• Cell types (brain vs. liver)
• Developmental (fetal vs. adult)
• Response to stimulus
• Gene activity (wild vs. mutant)
• Disease states (healthy vs. diseased)

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Expressed Genes

• Genes under a given condition
• mRNA extracted from cells
• mRNA labeled
• Labeled mRNA is mRNA present in a given condition
• Labeled mRNA will hybridize (base pair) with
  corresponding sequence on slide

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Two Different Types of Microarrays

• Custom spotted arrays (up to 20,000 sequences)
• cDNA
• Oligonucleotide
• High-density (up to 100,000 sequences) synthetic
  oligonucleotide arrays
• Affymetrix (25 bases)
• SHOW AFFYMETRIX LAYOUT

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Custom Arrays

• Mostly cDNA arrays
• 2-dye (2-channel)
• RNA from two sources (cDNA created)
• Source 1 labeled with red dye
• Source 2 labeled with green dye

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Two Channel Microarrays

• Microarrays measure gene expression
• Two different samples
• Control (green label)
• Sample (red label)
• Both are washed over the microarray
• Hybridization occurs
• Each spot is one of 4 colors

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Microarray Technology
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Microarray Image Analysis

• Microarrays detect gene interactions 4 colors
• Green high control
• Red High sample
• Yellow Equal
• Black None
• Problem is to quantify image signals

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Single Color Microarrays

• Prefabricated
• Affymetrix (25mers)
• Custom
• cDNA (500 bases or so)
• Spotted oligos (70-80 bases)

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Microarray Animations

• Davidson University
• http//www.bio.davidson.edu/courses/genomics/chip/
  chip.html
• Imagecyte
• http//www.imagecyte.com/array2.html

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Basic idea of Microarray

• Construction
• Place array of probes on microchip
• Probe (for example) is oligonucleotide 25 bases
  long that characterizes gene or genome
• Each probe has many, many clones
• Chip is about 2cm by 2cm
• Application principle
• Put (liquid) sample containing genes on
  microarray and allow probe and gene sequences to
  hybridize and wash away the rest
• Analyze hybridization pattern

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Microarray analysis
Operation Principle Samples are tagged with
flourescent material to show pattern of
sample-probe interaction (hybridization) Micro
array may have 60K probe
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(No Transcript)
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Gene Expression Data

• Gene expression data on p genes for n samples

mRNA samples
sample1 sample2 sample3 sample4 sample5 1
0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49
0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10
0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.
06 1.06 1.35 1.09 -1.09 ...
Genes
Gene expression level of gene i in mRNA sample j
Log (Red intensity / Green intensity)

Log(Avg. PM - Avg. MM)
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Some possible applications

• Sample from specific organ to show which genes
  are expressed
• Compare samples from healthy and sick host to
  find gene-disease connection
• Probes are sets of human pathogens for disease
  detection

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Huge amount of data from single microarray

• If just two color, then amount of data on array
  with N probes is 2N
• Cannot analyze pixel by pixel
• Analyze by pattern cluster analysis

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Major Data Mining Techniques

• Link Analysis
• Associations Discovery
• Sequential Pattern Discovery
• Similar Time Series Discovery
• Predictive Modeling
• Classification
• Clustering

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Cluster Analysis Grouping Similarly Expressed
Genes, Cell Samples, or Both

• Strengthens signal when averages are taken within
  clusters of genes (Eisen)
• Useful (essential ?) when seeking new subclasses
  of cells, tumours, etc.
• Leads to readily interpreted figures

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Some clustering methods and software

• PartitioningK-Means, K-Medoids, PAM, CLARA
• HierarchicalCluster, HAC?BIRCH?CURE?ROCK
• Density-based CAST, DBSCAN?OPTICS?CLIQUE
• Grid-basedSTING?CLIQUE?WaveCluster
• Model-basedSOM (self-organized
  map)?COBWEB?CLASSIT?AutoClass
• Two-way Clustering
• Block clustering

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Assessment of various methods

• Algorithmic Approaches to Clustering Gene
  Expression Data, Ron Shamir School of Computer
  Science, Tel-Aviv University Tel-Aviv
• http//citeseer.nj.nec.com/shamir01algorithmic.htm
  l
• Conclusion hierarchical clustering exceptional

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Partitioning

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Density-based clustering
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Hierarchical (used most often)

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Hierarchical Clustering grouping similarly
expressed genes
Gene Expression Profile Analysis
Sample
.
B
C
A
gene
0.4 0.9 0 0.5 .. .. 0.8
0.2 0.8 0.3 0.2 .. .. 0.7
0.6 0.2 0 0.7 .. .. 0.3

1 2 3 4 ..
.. 1000
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After Clustering
Gene Expression Profile Analysis
sample
.
B
C
A
gene
.. 0 0.4 0.5 .. 0.9 0.8
.. 0.3 0.2 0.2 .. 0.8 0.7
.. 0 0.6 0.7 .. 0.2 0.3

.. 3 1 4 ..
2 1000
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randomized row column both

data clustered
Eisen et al. Proc. Natl. Acad. Sci. USA 95 (1998)
time
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Types of Similarity Measurements

• Distance measurements
• Correlation coefficients
• Association coefficients
• Probabilistic similarity coefficients

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Correlation Coefficients

• The most popular correlation coefficient is
  Pearson correlation coefficient (1892)
• correlation between XX1, X2, , Xn and YY1,
  Y2, , Yn
• where

sXY is the similarity between X Y
sXY
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Use of Similarity for Tree Construction

• Normalize similarity so that 1
• Then have nxn similarity matrix S whose diagonal
  elements are 1
• Define distance matrix by (for example)
• D 1 S
• Diagonal elements of D are 0
• Now use distance matrix to built tree (using some
  tree-building software recall lecture on
  Phylogeny)

sXX
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A dendrogram (tree) for clustered genes
E.g. p5

• Let p number of genes.
• 1. Calculate within class correlation.
• 2. Perform hierarchical clustering which will
  produce (2p-1) clusters of genes.
• 3. Average within clusters of genes.
• 4 Perform testing on averages of clusters of
  genes as if they were single genes.

Cluster 6(1,2)
Cluster 7(1,2,3)
Cluster 8(4,5)
Cluster 9 (1,2,3,4,5)
1
2
3
4
5
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A real case
Nature Feb, 2000 Paper by Allzadeh. A et
al Distinct types of diffuse large B-cell
lymphoma identified by gene expression
profiling
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Validation Techniques Huberts G Statistics

• XX(i, j) and YY(i, j) are two n n matrix
• X(i, j) similarity of gene i and gene j
• Huberts G statistic represents the point serial
  correlation
• where M n (n - 1) / 2
• A higher value of G represents the better
  clustering quality.

if genes i and j are in same cluster, otherwise
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Discovering sub-groups
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Gene Expression is Time-Dependent
Time Course Data
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Sample of time course of clustered genes
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Limitations

• Cluster analyses
• Usually outside the normal framework of
  statistical inference
• Less appropriate when only a few genes are likely
  to change
• Needs lots of experiments
• Single gene tests
• May be too noisy in general to show much
• May not reveal coordinated effects of positively
  correlated genes.
• Hard to relate to pathways

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Useful Links

• Affymetrix www.affymetrix.com
• Michael Eisen Lab at LBL (hierarchical clustering
  software Cluster and Tree View (Windows))
  rana.lbl.gov/
• Review of Currently Available Microarray
  Softwarewww.the-scientist.com/yr2001/apr/profile1
  _010430.html
• ArrayExpress at the EBI http//www.ebi.ac.uk/array
  express/
• Stanford MicroArray Database http//genome-www5.st
  anford.edu/
• Yale Microarray Database http//info.med.yale.edu/
  microarray/
• Microarray DB www.biologie.ens.fr/en/genetiqu/puce
  s/bddeng.html