Microarray analysis

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Microarray analysis

• Quantitation of Gene Expression
• Expression Data to Networks

BIO520 Bioinformatics Jim Lund
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Microarray data

• Image quantitation.
• Normalization
• Find genes with significant expression
  differences
• Annotation
• Clustering, pattern analysis, network analysis

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Sources of Non-Biological Variation

• Dye bias differences in heat and light
  sensitivity, efficiency of dye incorporation
• Differences in the amount of labeled cDNA
  hybridized to each channel in a microarray
  experiment (Channel is used to refer to a
  combination of a dye and a slide.)
• Variation across replicate slides
• Variation across hybridization conditions
• Variation in scanning conditions
• Variation among technicians doing the lab work.

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Factors which impact on the signal level

• Amount of mRNA
• Labeling efficiencies
• Quality of the RNA
• Laser/dye combination
• Detection efficiency of photomultiplier or CCD

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Hela HepG2
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Hela HepG2
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M vs. A Plot
M Log Red - Log Green
A (Log Green Log Red) / 2
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M v A plots of chip pairs before normalization
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M v A plots of chip pairs after quantile
normalization
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Types of normalization

• To total signal (linear normalization)
• LOESS (LOcally WEighted polynomial regreSSion).
• To house keeping genes
• To genomic DNA spots (Research Genetics) or mixed
  cDNAs
• To internal spikes

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Fold change the crudest method of finding
differentially expressed genes
Hela HepG2
gt2-fold expression change
gt2-fold expression change
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What do we mean by differentially expressed?

• Statistically, our gene is different from the
  other genes.

Distribution of average ratios for all genes
Number of genes
Log ratio
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Finding differentially expressed genes What
affects our certainty that a gene is up or
down-regulated?

• Number of sample points
• Difference in means
• Standard deviations of sample

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Practical views on statistics

• With appropriate biological replicates, it is
  possible to select statistically meaningful
  genes/patterns.
• Sensitivity and selectivity are inversely
  related - e.g. increased selection of true
  positives WILL result in more false positive and
  less false negatives.
• False negatives are lost opportunities, false
  positives cost s and waste time.
• A typical set of experiments treated with
  conservative statistics typically results in more
  genes/pathways/patterns than one can sensibly
  follow - so use conservative statistics to
  protect against false positives when designing
  follow-on experiments.

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Statistical Tests

• Students t-test
• Correct for multiple testing! (Holm-Bonferroni)
• False discovery rate.
• Significance Analysis of Microarrays (SAM)
• http//www-stat.stanford.edu/tibs/SAM/
• ANOVA
• Principal components analysis
• Special methods for periodic patterns in data.

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Volcano plot log(expr) vs p-value
p-value
Log(fold change)
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Scatter plot showing genes with significant
p-values
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Pattern finding

• In many cases, the patterns of differential
  expression are the target (as opposed to specific
  genes)
• Clustering or other approaches for pattern
  identification - find genes which behave
  similarly across all experiments or experiments
  which behave similarly across all genes
• Classification - identify genes which best
  distinguish 2 or more classes.
• The statistical reliability of the pattern or
  classifier is still an issue and similar
  considerations apply - e.g. cluster analysis of
  random noise will produce clusters which will be
  meaningless.

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What is clustering?

• Group similar objects together.
• Genes with similar expression patterns.
• Objects in the same cluster (group) are more
  similar to each other than objects in different
  clusters.

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Clustering

• What is clustering?
• Similarity/distance metrics
• Hierarchical clustering algorithms
• Made popular by Stanford, ie. Eisen et al. 1998
• K-means
• Made popular by many groups, eg. Tavazoie et al.
  1999
• Self-organizing map (SOM)
• Made popular by Whitehead, ie. Tamayo et al.
  1999

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Typical Tools

• Expression NTI
• GeneSpring
• Affymetrix GeneChip Operating System (GCOS)
• Cluster/Treeview
• R statistics package microarray analysis
  libraries.

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How to define similarity?
Experiments
genes
X
n
1
p
1
X
genes
genes
Y
Y
n
n
Raw matrix
Similarity matrix

• Similarity metric
• A measure of pairwise similarity or
  dissimilarity
• Examples
• Correlation coefficient
• Euclidean distance

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Similarity metrics

• Euclidean distance
• Correlation coefficient

Euclidean clustering magnitude
Direction Correlation clustering direction
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Sporulation-example
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Sporulation-example
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Self-organizing maps (SOM) Kohonen 1995

• Basic idea
• map high dimensional data onto a 2D grid of nodes
• Neighboring nodes are more similar than points
  far away

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Self-organizing maps (SOM)
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SOM Clusters
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Inference

• NDT80 transcription factor
• Can account for control of many, not ALL, genes
  with pattern
• How do we find the other factor(s)
• Infer binding site
• DNA binding protein selection?

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Inferences from Expression

• Pathways not known to be involved
• Ontology?
• Novel genes involved in a known pathway
• like and unlike tissues

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Transcription FactorsRegulatory Networks

• ID co-regulated genes
• Search for common motifs
• Evaluate known motifs/factors
• Search for new ones.
• Programs MEME, etc.

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mRNA-protein Correlation

• YPD should have relevant data
• will yeast be typical?
• Electrophoresis 18533
• 23 proteins on 2D gels
• r0.48 for mRNAprotein
• Posttranscriptional and post translational
  regulation important!

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Drosophila Fusion Project
Lots of introns

• Exon GFP vector
• Good site?
• Fluorescent sort
• Image

Lynne Cooley
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Developmental Localization
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Other microarray formats

• Single nucleotide polymorphism (SNP) chips
• Oligos with each of 4 nt at each SNP.
• Chromosomal IP chips (ChIPchip)
• Determine transcription factor binding sites
• Promoter DNA on the chip.
• Alternative splicing chips
• Long oligos, covering alternatively spliced
  exons, or all exons.
• Genome tiling chips

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ChIPchip--Identification of Transcription Factor
Binding Sites

• Cross link transcription factors to DNA with
  formaldehyde
• Pull out transcription factor of interest via
  immunoprecipitation with an antibody or by
  tagging the factor of interest with an isolatable
  epitope (e.g GST fusion).
• Fractionate the DNA associated with the
  transcription factor, reverse the cross links,
  label and hybridize to an array of protomer DNA.
• Brown et.al. (2001) Nature, 409(533-8)

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Analysis of TF Binding Sites
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On to Proteomics
DNA?RNA ?Protein