microarray - Angelfire · Cluster Analysis Single array not suitable Functional analysis...
Transcript of microarray - Angelfire · Cluster Analysis Single array not suitable Functional analysis...
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Microarray Technique
Some background
M. Nath
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Outline
Introduction
Spotting Array Technique
GeneChip Technique
Data analysis
Applications
Conclusion
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Now
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Blind Guess?
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Functional Pathway
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MicroarrayTechnique
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Principle
Comprehensive functional analysis of genome
Simultaneous analysis of patterns of gene expression
Genome > Transcriptome > Proteome
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Types of Microarray
cDNA Array (Brown et. al., 1995)
Genomic DNA Array (DeRisi et. al., 1997)
Oligonucleotide Array (Morton et. al., 1998)
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Spotted Array Technology
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LibrarySpotted Array Technology
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An array of slides is printedSlides can be glass or nylon
Printing SlidesSpotted Array Technology
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Spotted Array Technology
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Hybridisation of Slides
Slide developer
Spotted Array Technology
Up to 48 slides are developed under uniform conditions
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ScanningConfocal laser scanner is used
Two different lasers to read Red and Green dye intensities
Imaging software reads Red & Green intensity for each dot applied
A Graphic image is savedLaser Scanner
Spotted Array Technology
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Results
Green = Active in Sample 1Red = Active in Sample 2Yellow = Active in both samplesBlack = Active in neither
Spotted Array Technology
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GeneChip TechnologyAffymetrix chip
Oligos of 25 nt long
40 oligos for detection of each gene
11-20 oligos as Perfect Match (PM)
11-20 oligos as Mismatch (MM) at position 13
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GeneChip Technology
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GeneChip Technology
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Spotted Array Technology
10000No. of genes / array
1Probes pair per gene
10-20 µg total RNAStarting material
RoutineFeatures
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Spotted Array Technology
Laborious
Inexpensive
Moderate specificity
Moderate representation
Low Density
Cannot detect polymorphism
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GeneChip Technology
4000012000No. of genes / array
420Probes pair per gene
93% identity
70-80% identity
Discrimination of related genes
1:1061:105Detection specificity
2 ng total RNA
5 µg total RNAStarting material
LimitRoutineFeatures
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GeneChip Technology
Easy
Expensive
High specificity
High representation
High density
Can detect polymorphism
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Data Analysis
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ScalingLinearity
Data Analysis
Gene intensity Chip 1
Gen
e in
tens
ity C
hip
2
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Scaling
Linear and non-linear models
Constitutively and constantly expressed Maintenance gene
More genes on chip
Data Analysis
Gene intensity Chip 1G
ene
inte
nsity
Chi
p 2
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Outlier
Two chips may differ in expression for same gene
If one replicate deviates several standard deviation from mean, remove it
Data Analysis
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Data Analysis
Absolute measurements
AvgDiffΣ ( PMn – MMn ) / N
Weighted AvgDiffΣ ( PMn – MMn ) φn / N
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Fold Change
Log2 of ratio of intensities after being corrected for background
E.g. Log2 (Sample / Control) = Log2 (Red / Green)
=1 : unchanged; >1 : upregulated; <1 : downregulated
Affymetrix chip (AffyFold)(Sample - Control) / Min (Sample, Control)
Data Analysis
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Significance Test
Test of significancet-test with unequal varianceANOVA and F testREML
Non-parametric testsWilcoxon testMann-Whitney rank sum test
Correction for multiple testingBonferroni correction
Data Analysis
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Cluster AnalysisSingle array not suitable
Functional analysisCo-regulationNew gene discovery
Samples collected temporally, spatially …
Multiple array & Cluster analysis
Clustering of similarly behaving genes
Genes with similar functions generally cluster together
Data Analysis
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Cluster Analysis
Cluster analysisHierarchical clusteringK-means clusteringSelf Organising MapsDistance measures
Data Analysis
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Beyond Clustering
Discovery of regulatory elements in promoter regionIdentifying regulatory networks
Time series approachSteady-state approachNeural network technique
Selection of genesGene findingSelection of regions within the genesSelection of PCR primersSelection of unique oligomer probes
Data Analysis
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Software Package
Affymetrix Data Mining ToolAffymetrix NetAffxBiomax Gene Expression Analysis SuiteGeneData ExpressionistInformax XpressionInvitrogen Corp. ResGen PathwaysRosetta Resolver Gene Expression SystemSilicon Genetics GeneSpringSpotfire
Data Analysis
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Applications
Analysis of patterns of gene expressionFunctional relationship between genes
Expression in coregulatory gene group
Monitoring changes in genomic DNACellular pathways affected by mutation
Changes in expression profiles of mutants
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Applications
Simultaneous detection of many genes
Gene discovery
Pathway analysis
Molecular basis of disease progression
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Applications
Molecular signatures of pathogens
Comparative genomic studies of pathogensVirulence difference
Pathogen genetics and manifestationLife cycleReplication, translational control
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Applications
Host-parasite interaction
Pathogen establishmentHost cell recognitionHost cell responseParasite response to host immune response
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Applications
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Constraints
Complex system of eukaryotes & multi-cellular organisms
Transcriptome analysis
Developing technology
Many stages
Design of experiment
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Constraints
Array quality
Highly variable data
Analysis of data
Published experiments
Cost
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From Here to Tomorrow
Recent & Powerful
More improvementProtocolHardwareExperimental designComputational technique
Integrate with other data
Reproducible, fast, sensitive & economic
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