Microarrays
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Outline
• Intro to the technology
• Sample QC
• Experimental design
• Gene expression microarrays
• SNP microarrays for SNP and CNV analyses
• Methylation microarrays
• Applications
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Intro to Microarray
• A DNA microarray is a multiplex technology consisting
of thousands of oligonucleotide that are attached to a
solid surface and are hybridized to a biological sample.
• Several commercial platforms are available: Affymetrix,
Illumina, Agilent, etc.
Advantages
Disadvantages
Off-the- shelf product
Not flexible
Uniform and reproducible
(QC by manufacturer)
Known genes/transcripts
For specific species and
organisms
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The BeadArray Technology
• Silica beads that randomly self assemble
in microwells.
• Each bead contains X100,000 copies of a specific oligo.
• ~30-fold redundancy for each
bead.
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Applications
1975176103_A AVG_Signal vs 1975176103_B AVG_Signal
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• SNP genotyping
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1975176103_A AVG_Signal
• Gene expression
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1975176103_B AVG_Signal
• CNV analysis
• DNA methylation
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DNA/RNA Quality
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Automated Extraction and Purification
• DNA/RNA extraction and purification
• Spin-column kits
• Up to 12 samples
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DNA/RNA Quality
• Automated electrophoresis by TapeStation, Agilent
• QC of DNA/RNA
• Sizing
• Quantitation
• Level of degradation
• 28S rRNA/18S rRNA ratio
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DNA Quality
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DNA Quality
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RNA Quality
• Purity: A260/A280 = 1.9-2.1
• Integrity: 28S rRNA/18S rRNA = 1.8
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RNA Quality
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RNA Quality
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Experimental Design
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Experimental Design
• Define your biological question.
• Consider the expected effect and according to this
decide the sample size.
- For GT experiment (GWAS): the number of cases and
controls is determined by the study power.
- For GX experiment: at least three biological (not
technical) replicates, better four. Optional- pooling.
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Gene Expression Microarrays
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RNA Labeling and Amplification
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Direct Hybridization Assay
• 750 ng of aRNA.
• Array hybridization, washing, blocking and
streptavidin-Cy3 staining.
• Quantitatively detection of Cy3 fluorescence.
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Gene Expression Data Analysis
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Clustering Analysis
• % of variance component
• 3D PCA (Principal Component Analysis)
• Hierarchical clustering (heat map
and dendogram)
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The problem of Multiple Comparisons
• When doing so many statistical tests, it is almost
guaranteed that many of your results will be
incorrectly considered significant.
• At p-value = 0.05, 5 of every 100 results might be
called significant by mistake (p<0.05).
• But when doing ~20,000 tests per experiment (as
with microarrays and RNAseq), p-value of 0.05 means
that 1000 genes will be called significant by mistake.
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The Solution for Multiple Comparisons
• Filter out transcripts with expression at background
level or transcripts with less than 5% variation between
samples.
• Bonferroni correction: p/number of tests so,
0.05/20,000 = 2.5x10^-6. Thus, you should consider a
result significant if p<2.5x10^-6.
• False Discovery Rate (FDR): the expected proportion of
false-positives among the positive results. At adjusted
p=0.05, if you call 1000 genes significantly changed, up to
50 of those might be false positives, but the rest will be
true positives.  q-value/adjusted p-value
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Analysis of Variance (ANOVA)
• ANOVA allows us to compare means of ≥2 groups.
• You will get a list of significant differentially expressed
genes between groups (at an adjusted p-value of 0.05
and difference between the means of 1).
• Volcano plots (-log10 (p-value)=2  p-value=0.01
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Venn Diagram
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Pathway and Network Analysis
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SNP Microarrays
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ILM Whole-Genome SNP Genotyping
• Available for human and agriculture products
• Each array contains 700K-5M probes
• Off-the-shelf and custom arrays
• 200 ng gDNA (quantitation method: Qubit or
PicoGreen)
• SNPs can be used in GWAS for identification of
susceptibility loci (large groups of cases and controls)
and for analysis of copy number variation (CNVs)
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Infinium Assay Workflow
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Norm Theta
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Data Analysis for SNPs
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CNV Analysis
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Milestones in Cytogenetics
1960s G-banding karyotype
1980s FISH
1990s CGH
aCGH
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Norm Theta
2005 NGS
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Detection of Structural Variation
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Data Analysis for CNVs
• B Allele Frequency (BAF)- expresses the proportion of
intensity contribution of a given allele, which indicates the
genotype.
BAF=Number of B alleles/Total number of alleles
• Log2 R Ratio (LRR)- expresses the signal intensity of each
probe, which indicates the copy number.
LRR=log2(Robserved/Rexpected)
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Data Analysis for CNVs
Normal (diploid)
Deletion (loss of one copy)
Duplication (gain of one copy)
Copy-Neutral LOH (UPD)
LRR
Intensity
BAF
Genotype
Also can detect: Amplification,
Unbalanced aberration,
Aneuploidy, and Mosaicism.39
Data Analysis for CNVs
Normal (diploid)
LRR=0
CN=2
BAF
AA=0/2=0
BB=2/2=1
AB=1/2=0.5
LRR
Intensity
BAF
Genotype
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Data Analysis for CNVs
Deletion (loss of one copy)
LRR = -0.5
CN=1
BAF
A/- = 0/1=0
B/- = 1/1=1
LRR
Intensity
BAF
Genotype
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Data Analysis for CNVs
Duplication (gain of one copy)
LRR=0.5
LRR
Intensity
BAF
Genotype
CN=3
BAF
AAA=0/3=0
BBB=3/3=1
AAB=1/3=0.33
ABB=2/3=0.67
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Data Analysis for CNVs
LRR=0
CN=2
BAF
AA=0/2=2
BB=2/2=1
AB=Not present
Copy-Neutral LOH (UPD)
LRR
Intensity
BAF
Genotype
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Methylation Microarrays
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Methylation
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G
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ILM Whole-Genome Methylation
• > 850K methylation sites that covers:
- 99% of RefSeq genes, ~20 CpG sites per gene
distributed across the promoter, 5’UTR, first
exon, gene body, and 3’UTR.
- 96% of CpG islands.
- CpG sites outside of CpG islands.
- Non-CpG methylated sites identified in human stem cells.
- Differentially methylated sites in tumors.
- Diff methy sites across tissues.
- CpG islands outside of coding regions.
- miRNA promoter regions.
• Experimental design should consider effect of age, gender
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and tissue, on the methylation profile.
Methylation Assay
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Summary
• Microarrays technology is a powerful tool in
research.
• Microarrays provide insight for basic mechanism,
disease subtypes, treatment adjustment, and
diseases risks.
• Data analysis of microarrays has been standardized
and package into easy to use software tools for
biologists.
• However the success of experiment depends on
experimental design, biological samples collection,
and carful statistical analysis.
• Expected to be totally replaced soon by the NGS
technology (already happened for gene expression).
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Thank you!
Questions?
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