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Analysis of Microarray Data
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Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Analysis of Microarray Data
1.
2.
3.
4.
5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Analysis of Microarray Data
1.
2.
3.
4.
5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Quantification of Spot Intensity
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BlueFuse
QuantArray
ScanAlyze
NHGRI – Scanalytics
Imagene
GenePix
ArrayVision
TIGR Spotfinder
BlueFuse
Analysis of Microarray Data
1.
2.
3.
4.
5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Normalization
• To identify and remove sources of
systematic variation in the measured
fluorescence intensities due to factors
other than differential expression:
– Spatial effects
– Dye effects
• Comparison of expression levels within
and between microarrays.
Spatial Effects
MA Plots
Before normalization
M = log2R – log2G
A = (log2R+ log2G)/2
After normalization
Analysis of Microarray Data
1.
2.
3.
4.
5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Analysis of Data
GeneSpring
Analysis of Microarray Data
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3.
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5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Identification of Genes of Interest
• Microarray analysis can identify
hundreds of differentially expressed
genes.
• How do you identify genes of
particular interest?
Data Mining Tools
• Literature searches
– Chilibot (www.chilibot.net)
– Protein Annotator’s Assistant (www.ebi.ac.uk/paa)
– iHOP (www.pdg.cnb.uam.es/UniPub/iHOP)
• Pathway searches
– DAVID & EASE (www.david.niaid.nih.gov/david)
– Biorag (www.biorag.org)
• Transcription factor binding sites
– MatInspector (www.genomatrix.de)
Analysis of Microarray Data
1.
2.
3.
4.
5.
6.
Scan the images
Quantify intensity of spots
Normalization
Analysis of data
Identification of genes of interest
Validation
Validation
• Confirm microarray results by:
– qRT-PCR
– Northern blots
• Confirm biological significance by
investigating protein expression:
– Western blots
Conclusions (3)
• Several stages are involved in analyzing
microarray data:
– Scan images
– Quantify spots
– Statistical analysis
• There are many different soft-ware packages
and methods for this analysis.
• Further analysis is required to pin-point genes
of interest.
• Microarray results need to be validated by other
methods.
Conclusions (general)
• Microarray experiments are expensive.
• Microarray experiments are time-consuming.
• Microarray experiments generate huge
amounts of data.
• The quality of the data obtained from a
microarray experiment depends on:
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Design of the experiment
Quality of RNA
Statistical analysis
Interpretation of the data
• There is, at present, no definitive method for
either designing or analyzing a microarray
experiment.