Analysis and Interpretation of Microarray Data

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Analysis and Interpretation of Microarray Data

• Michael F. Miles, M.D., Ph.D.
• Depts. of Pharmacology/Toxicology and Neurology
  and the Center for Study of Biological Complexity
• Virginia Commonwealth University
• Richmond, VA
• mfmiles_at_vcu.edu

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Expression Profiling A Non-biased, Genomic
Approach to Resolving the Mechanisms of Addiction
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High Density DNA Microarrays
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Oligonucleotide Array Analysis
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Stepwise Analysis of Microarray Data

• Low-level analysis -- image analysis, expression
  quantitation
• Primary analysis -- is there a change in
  expression?
• Secondary analysis -- what genes show correlated
  patterns of expression? (supervised vs.
  unsupervised)
• Tertiary analysis -- is there a phenotypic
  trace for a given expression pattern?

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Primary Analysis (MAS-5, S-score, d-chip, PDNN)
Normalize, De-noise
Statistical Filtering (e.g. SAM)
GE Database (SQL Server)
Filtered Gene Lists
Overlay Biological Databases (PubGene, GenMAPP,
EASE, WebQTL, etc.)
Clustering Techniques
Hybridization and Scanning
Provisional Gene Patterns
Experimental Design
Molecular Validation (RT-PCR, in situ, Western)
Candidate Genes
Behavioral Validation
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Quality Assessment

• Gene specific R/G correlation, BG, spot,
  biological variation
• Array specific normalization factor, genes
  present, linearity, control/spike performance
  (e.g. 5/3 ratio, intensity)
• Across arrays linearity, correlation,
  background, normalization factors

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Sources of Variance in Microarray Experiments
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Chip Normalization Procedures

• Whole chip intensity
• Assumes relatively few changes, uniform
  error/noise across chip and abundance classes
• Linear vs. piece wise linear (quantile, lowess)
• Spiked standards
• Requires exquisite technical control, assumes
  uniform behavior
• Internal Standards
• Assumes no significant regulation

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Slide Normalization Pieces and Pins
Lowess normalization, Pin-specific Profiles
After Print-tip Normalization
See also Schuchhardt, J. et al., NAR 28 e47
(2000)
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Affymetrix Arrays PM-MM Difference Calculation
Probe pairs control for non-specific
hybridization of oligonucleotides
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Probe Level Analysis Challenges

• Large variability in PM and MM intensities
• Only 11-25 probe pairs
• MM is a complex mixture of true signal and
  background
• Normalization required to compare across chips
• Intensity dependent noise
• Etc.

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Probe Level Analysis Methods

• AvgDiff -- Affymetrix 1996, trimmed mean with
  exclusion of outliers, PM-MM
• MAS 5 -- Affymetrix 2001, modeled correction of
  MM, Tukeys bi-weight, PM-MM or PM-m
• MBEI -- Li and Wong 2001, modeled correction and
  outlier detection, PM-MM or PM only
• RMA (Robust Multichip Analysis) -- Irizarry et
  al. 2002, PM only
• PDNN (Position Dependent Nearest Neighbor) -
  Zhang et al. 2003, thermodynamic model for probe
  interactions, PM only

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MAS 5 Fold-Change vs. S-scores
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Secondary Analysis Expression Patterns

• Supervised multivariate analyses
• Support vector machines
• Non-supervised clustering methods
• Hierarchical
• K-means
• SOM

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AvgDiff
S-score
Use of S-score in Hierarchical Clustering of
Brain Regional Expression Patterns
PFC
VTA
NAC
PFC
NAC
VTA
HIP
HIP
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Tertiary Analysis Connecting Function with
Expression Patterns

• Annotation
• UniGene/Swiss-Prot, SOURCE, DAVID
• Biased functional assessment
• Manual, GenMAPP, GeneSpring
• Non-biased functional queries
• PubGen
• MAPPFinder, DAVID/Ease, GEPAS, GOTree Machine,
  others
• Overlaying genomics and genetics
• WebQTL

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Non-biased (semi) Functional Group Analysis
GenMAPP
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Expression Analysis Systematic Explorer -- EASE
http//apps1.niaid.nih.gov/david/upload.jsp
Genome Biol. 20034(10)R70. Epub 2003 Sep 11.
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EASE -- Options in Analysis
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Efforts to Integrate Diverse Biological Databases
with Expression Information PubGen
www.PubGen.org
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B6 Et D2 Et B6/D2
B6 Et D2 Et B6/D2
B6 Et D2 Et B6/D2
Functional Annotation Association Mining (EASE)
High-throughput Literature Association Mining
(PubGene)
Genetic Associations (WebQTL)
Additional Expression Associations (Molecular
Triangulation)
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