BIOS816/VBMS818 Lecture 8

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BIOS816/VBMS818 Lecture 8 Microarray Analysis

• Guoqing Lu
• Office E115 Beadle CenterTel (402)
  472-4982Email glu3_at_unl.eduWebsite
  http//biocore.unl.edu

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Introduction to DNA Microarray

• Microarray revolutionized biology and medicine
  research
• One gene at a time before, now tens of thousands
  simultaneously
• Gene expression
• Measure the expression levels of many thousands
  of genes in only a few biological samples
• E.g., sample from specific organ to show which
  genes are expressed
• E.g., compare samples from healthy and sick host
  to find gene-disease connection
• E.g., probes are sets of human pathogens for
  disease detection

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Introduction to DNA Microarray (contd)

• Replicates are needed
• Technical replicates, i.e. measuring gene
  expression with the same starting material on
  independent arrays
• Biological replicates, e.g. measuring gene
  expression from multiple cell lines
• The challenge to the biologist is to apply
  appropriate statistical techniques to determine
  which changes are relevant

Affymetrix U133 Plus 2.0 47,000 x 11 x 2
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Microarray Experiment

• http//www.bio.davidson.edu/courses/genomics/chip/
  chip.html

Flash Animation
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Microarray Technology

• Basic principle is the same
• DNA complementary to genes of interest is
  generated and laid out in microscopic quantities
  on solid surfaces at defined positions
• DNA from samples is eluted over the surface,
  complementary DNA binds
• Presence of bound DNA is detected by florescence
  following laser excitation

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Two Different Techniques

• Spotted cDNA
• DNA sequences are laid down through spotting
• Complete sequences are laid down
• Cheaper
• Usually measures relative expression in two
  samples
• E.g., Systeni/Stanford
• Oligonucleotide arrays
• DNA sequences are laid down through
  photolithography
• a series of fragments are laid down
• Probably give higher quality results
• Usually measures expression in a single sample
• Mainly supplied by Affymetrix Inc.

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Spotted cDNA

• Uses available cDNA libraries to create the array
• Quality depends on choice of cDNAs
• Cross hybridization and non-specific binding can
  be a problem
• Reduce cross hybridization by choosing highly
  gene specific DNA for the spots

mRNA
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Oligonucleotide arrays

• Each gene represented by 11 20 paired oligos
• Each represents a different part of the gene
• Oligos are produced in situ on the chip
• Each pair comprises two 25-mers
• Perfect match (PM)
• Mismatch (MM)

Affymetrix uses a unique combination of
photolithography and combinatorial chemistry to
manufacture GeneChip Arrays.
http//www.affymetrix.com/technology/manufacturing
/index.affx
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PM and MM oligos
PM ATCTGCGTGTCGTAGTGTGACCCCA MM
ATCTGCGTGTCGAAGTGTGACCCCA

• By measuring the difference in hybridization
  to the PM and MM oligos the effect of
  non-specific and cross hybridization is minimised
• Using 11-20 pairs from different parts of the
  gene these effects are further reduced

https//www.affymetrix.com/support/downloads/manua
ls/data_analysis_fundamentals_manual.pdf
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Microarray Data Analysis

• Data preprocessing
• allow data sets from two (or more) samples to be
  compared to each other
• Inferential statistics
• hypothesis testing
• the likelihood that particular genes are
  significantly regulated
• Descriptive (exploratory) statistics
• clustering and principal components analysis
• inspect the complex data set for biologically
  meaningful patterns

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Microarray data analysis

• Begin with a data matrix (gene expression values
  versus samples)
• Typically, there are many genes (gtgt 10,000) and
  few samples ( 10)

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Preprocessing normalization

• Normalization is needed
• To compare signal intensities on two arrays
• To compare two mRNA samples on the same array
• Make sure the samples are equivalent in some sense

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Normalization methods

• Adjust spot values to show
• Same total mRNA in all samples
• Or, Same expression level for certain
  housekeeping genes
• Use spiked controls
• Add equal amounts of a different mRNA to each
  sample and normalize to equalize intensity for
  these spots

cDNA Array
Oligo Array
Both Arrays
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Data analysis global normalization

• Global normalization procedure
• Step 1 subtract background intensity values (use
  a blank region of the array)
• Step 2 globally normalize so that the average
  ratio 1 (apply this to 1-channel or 2-channel
  data sets)

Do Exercise!
Affymetrix scaling!!!
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Scatter plots

• Useful to represent gene expression values from
  two microarray experiments (e.g. control,
  experimental)
• Each dot corresponds to a gene expression value
• Most dots fall along a line
• Outliers represent up-regulated or down-regulated
  genes

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Inferential statistics

• Inferential statistics are used to make
  inferences about a population from a sample.
• Hypothesis testing is a common form of
  inferential statistics
• A null hypothesis is stated, such as There is
  no difference in signal intensity for the gene
  expression measurements in normal and diseased
  samples.
• The alternative hypothesis is that there is a
  difference.
• We use a test statistic to decide whether to
  accept or reject the null hypothesis. For many
  applications, we set the significance level a to
  p lt 0.05.

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Inferential statistics
Paradigm Parametric test Nonparametric
Compare two unpaired groups Unpaired t-test Mann-Whitney test
Compare two paired groups Paired t-test Wilcoxon test
Compare 3 or more groups ANOVA
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Significance analysis of microarrays (SAM)

• SAM
• an Excel plug-in
• modified t-test
• adjustable false discovery rate

http//www-stat.stanford.edu/tibs/SAM/
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SAM
up- regulated
observed
expected
down-regulated
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Descriptive statistics

• Microarray data are highly dimensional there are
  many thousands of measurements made from a small
  number of samples.
• Descriptive (exploratory) statistics help you to
  find meaningful patterns in the data.
• A first step is to arrange the data in a matrix.
• Next, use a distance metric to define the
  relatedness of the different data points.
• Two commonly used distance metrics are
• Euclidean distance
• Pearson coefficient of correlation

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Descriptive statistics clustering

• Clustering algorithms offer useful visual
  descriptions of microarray data.
• Genes may be clustered, or samples, or both.
• This may be agglomerative (building up the
  branches of a tree, beginning with the two most
  closely related objects) or divisive (building
  the tree by finding the most dissimilar objects
  first).
• In each case, we end up with a tree having
  branches and nodes.

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agglomerative
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a
a,b
b
a,b,c,d,e
c
c,d,e
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d,e
e
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divisive
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Cluster and TreeView

• Perform a variety of types of cluster analysis
  and other types of processing on large microarray
  datasets
• Clustering
• K means
• SOM
• PCA

http//rana.lbl.gov/EisenSoftware.htm
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Cluster and TreeView
http//rana.lbl.gov/manuals/ClusterTreeView.pdf
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Cluster and TreeView
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Two-way clustering of genes (y-axis) and cell
lines (x-axis) (Alizadeh et al., 2000)
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K-means clustering

• Clusters the expression profiles into K clusters
• You have to specify K
• Produces clusters that are as tight as possible
• Each cluster has a centroid or mean expression
  profile
• Tightness of clusters measured by the sum of
  squared distances between each gene and the
  centroid of its cluster
• Algorithm tries to minimise this sum

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Self-organizing maps (SOM)

• Unlike k-means clustering, which is unstructured,
  SOMs allow one to impose partial structure on the
  clusters.
• The principle of SOMs
• One chooses an initial geometry of nodes such
  as a 3 x 2 rectangular grid (indicated by solid
  lines in the figure connecting the nodes).
• Hypothetical trajectories of nodes as they
  migrate to fit data during successive iterations
  of SOM algorithm are shown.
• Data points are represented by black dots, six
  nodes of SOM by large circles, and trajectories
  by arrows.

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Microarray Software
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Exercise

• http//pevsnerlab.kennedykrieger.org/hinxton.html
  
• Thanks to Dr. Jonathan Pevsner