Download Gene Expression Analysis Using Microarrays

Gene Expression Analysis Using
Microarrays
Dr Mushtaq Ahmed
Technology Incubation Division
Persistent Systems Private Ltd
Pune
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Topics
1. Introduction
2. Data Storage and Exchange Standards
3. Analysis (Clustering)
4. Conclusion and References
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1. Introduction
• Structure Activity Relationship
• Structural vs. Functional Genomics
• Principals of Microarray Experiment
• Applications
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Structure Activity Relationship
GENES
(finite)
EXPERIMENTAL
SETUP
Structural
Genomics
OR
Prediction
Work
Functional
Genomics
OR
Confirmation
Work
PROTEINS
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FUNCTIONS
(infinite)
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Source:Yale
Bioinformatics
Principles of a Microarray Experiment:
Hybridization
1.
Environment  Functions  Proteins  mRNA  cDNA
2.
Different incubations of cells results in up or down regulation
of different sets of genes.
3.
Microarray provides a medium for matching known and
unknown DNA samples based on base-pairing rules and
automating the process of identifying the unknowns
4.
Set of expressed genes (at mRNA stage) isolated and
identified using hybridization on a microarray chip
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HTS Using Hybridization
Microarray
Chip
Probe: oligos/cDNA
(gene templates)
+
Target: cDNA
(variables to be detected)
Samples
Hybridization
Analysis of outcome
Pathways
Targets/Leads
Disease Class.
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Functional
Annotation
Physiological
states
Timeline for drug discovery
Discovery (5 yrs)
5000
Gene expression study
Pre-Clinical (1 yr)
50
Clinical (6 yrs)
5
Review (2 yrs)
1
Marketed
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2. Data Storage and Exchange
Standards
• Raw and Processed Data
• Conceptual View of Database
• Example of ArrayExpress
• Issues
• Standardization for Exchange
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Raw data – images
• Red (Cy5) dot
–
overexpressed or up-regulated
–
underexpressed or down-regulated
• Green (Cy3) dot
• Yellow dot
– equally expressed
• Intensity - “absolute” level
• red/green - ratio of expression
cDNA plotted microarray
–
–
2 - 2x overexpressed
0.5 - 2x underexpressed
• log2( red/green ) - “log ratio”
–
–
1
-1
2x overexpressed
2x underexpressed
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Microarray Expression Value
Representation
expression value types
composite
spots
primary
spots
primary
measurements
primary images
Source: MGED
derived
values
composite images
e.g., green/red ratios
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Gene expression database – a conceptual
view
Samples
Gene expression
matrix
Genes
Gene
annotations
Sample
annotations
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Gene expression
levels
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DAG Representation of Biomaterials
Sample source
Primary sample 1
Derived sample 1
Derived sample 2
treatment
A new state of
sample source
treatment
Primary sample 2
extraction
Extract 1
Extract 2
labeling
Hybridization
Labeled extract 2
Labeled extract 1
Source: MGED
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ArrayExpress (MGED) Design
Reference
e.g., publication, web
resource
Database
e.g., gene in
SWISS-prot
ArrayExpress
Ontology
e.g., organism
taxonomy
Experiment
Hybridization
Array
External links
Sample
Source: MGED
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ExpressionValue
ArrayExpress (MGED) Architecture
application server
Web server
MAML
data
ArrayExpress
data submission &
Curation database
Curation pipeline
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Pvt. Ltd.
image
server?
http://www.persistent.co.in
data warehouse
Source: MGED
Issues in Storage
• Size of Data
– Experiments
• 100 000 genes, 320 cell types
• 2000 compounds, 3 time points, 2 concentrations, 2 replicates
– Data
• 8 x 1011 data-points
• 1 x 1015 = 1 petaB of data
• Others
– Raw data are images
– lack of standard measurement units for gene expression
– lack of standards for sample annotation
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Standardization
• MIAME (Minimum Info About a Microarray Expt)
– Experimental design, Array design
– Samples, Hybridisations
– Measurements, Controls
• OMG-LSR-DFT
– Life Sciences Research, Domain Task Force Gene Expression RFP
– EBI (MAML), Rosetta (GEML), NetGenics : submitters
• Proposed MAGEML (MAML +GEML)
–
–
–
–
Annotations + data; data stored as a set of external 2D matrices
Data format independent of particular scanner or image analysis software
Sample and treatment can be represented as a Directed Acyclic Graphs
Concept of composite images and composite spots
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3. Data Analysis (Clustering)
• Normalization
• Hierarchical Clustering
• Divisive Clustering
• Other Methods
• Visual Tools
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Normalization
• Assumption
– Average expression ratio =1
– Amount of mRNA from both the sample is same
• Total Intensity
– Calculate a factor to rescale intensities of all te genes so that
• total Cy3= total Cy5
• Regression Techniques
– Adjust the intensities so that
• Slope of scatter plot of Cy3 vs Cy5 =1
• Using ratio statistics
– Based on ‘housekeeping genes’ expression a probability density
ratio is developed which is used for normalization
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Clustering
• Hierarchical
– Single, Complete and Average Linkage
• Divisive
– K-means
– Self Organizing Maps (SOM)
• Others
– Principal Component Analysis (PCA)
– Supervised Methods
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Hierarchical clustering
• Distance metrics or Similarity Measures
– Euclidian, Pearson, distance of slopes etc..
• Cost functions
– Single Linkage
• Min distance of any two members (one from each of the two clusters)
– Complete Linkage
• Max distance of any two members (one from each of the two clusters)
– Average Linkage
• UPGMA
• WPGMA
• Within Groups
– Ward’s Method
• Join which produces smallest possible error in some of squared errors
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Divisive clustering
•
K-means
– ‘k’ random (or specified) points used to create clusters, average vectors for
the clusters then used iteratively
– Knowledge of probable no of clusters (k) needed
– Used in combination with PCA and hierarchical clustering
•
Self Organizing maps
– User defined geometric configurations as partitions
– Random vectors generated for each partition and TRAINED till convergence
(ANN based)
•
Visualization Methods
– Helps in cluster visualization
• Scatter Plot, Web plot, histogram
– May help in clustering itself
• E.g., SuperGrouper utility of MaxdView
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Other Clustering Methods
•
PCA (Principal Component Analysis)
– Also called SVD (Singular Value Decomposition)
– Reduces dimensionality of gene expression space
– Finds best view that helps separate data into groups
•
Supervised Methods
– SVM (Support Vector Machine)
– Previous knowledge of which genes expected to cluster is used for training
– Binary classifier uses ‘feature space’ and ‘kernel function’ to define a optimal
‘hyperplane’
– Also used for classification of samples- ‘expression fingerprinting’ for disease
classification
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4. Conclusion and References
•
•
•
•
•
Microarrays makes HTS with hybridization possible
No single standard unit for measuring expression levels
Handling and interpretation not yet exact
Assumptions: Elements in cluster must share some commonality
Classification depends on method used for clustering, normalization,
distance function
• No “correct” way of classification, “biological understanding” is the
ultimate guide
• Provides extension to existing knowledge (e.g., classifying a novel
gene into a known pathway)
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Software
•
Databases
– Public repositories:
• GEO (NCBI), GeneX (NCGR), ArrayExpress (EBI)
– In-house databases
• Stanford, MIT, University of Pennsylvania,
– Organism specific databases
•
Mouse Genome Informatics Database
– Proprietary databases –
• Gene Logic, NCI, Synergy (NetGenics), Genomics Knowledge Platform (Incyte)
•
Analysis Tools
– Public Domain
• maxdView (University of Manchester)
• CyberT , RCuster interfaces of GeneX
– Proprietary
• Spotfire, Xpression NTI (Informaxinc)
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References
• Microarray Gene Expression Database Group
– http://www.mged.org
• National Center for Genomic Research
– http://genex.ncgr.org
• University of Manchester , Bioinformatics Group
– http://bioinf.man.ac.uk/microarray/resources.html
• Nature Reviews Genetics
– http://www.nature.com/nrg/
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