Persistent Systems Pvt. Ltd. Gene Expression Analysis Using Microarrays Dr Mushtaq Ahmed Technology...

Persistent Systems Pvt. Ltd.http://www.persistent.co.in

Gene Expression Analysis Using Microarrays

Dr Mushtaq Ahmed

Technology Incubation Division

Persistent Systems Private Ltd

Topics

1. Introduction

2.Data Storage and Exchange Standards

3.Analysis (Clustering)

4.Conclusion and References

1. Introduction

• Structure Activity Relationship

• Structural vs. Functional Genomics

• Principals of Microarray Experiment

• Applications

Structure Activity Relationship

GENES(finite)

FUNCTIONS(infinite)

PROTEINS

EXPERIMENTAL SETUP

FunctionalGenomics

ORConfirmation

StructuralGenomics

ORPrediction

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

HTS Using Hybridization

Target: cDNA (variables to be detected)

Probe: oligos/cDNA(gene templates) +

Hybridization

PathwaysFunctional Annotation

Analysis of outcome

Microarray Chip

Samples

Targets/Leads Disease Class.Physiological

states

Timeline for drug discovery

Discovery (5 yrs)5000 Gene expression

Pre-Clinical (1 yr)

Clinical (6 yrs)

Review (2 yrs)

Marketed

2. Data Storage and Exchange Standards

• Raw and Processed Data

• Conceptual View of Database

• Example of ArrayExpress

• Issues

• Standardization for Exchange

Raw data – images

• Red (Cy5) dot – overexpressed or up-regulated

• Green (Cy3) dot – underexpressed or down-regulated

• Yellow dot– equally expressed

• Intensity - “absolute” level

• red/green - ratio of expression– 2 - 2x overexpressed– 0.5 - 2x underexpressed

• log2( red/green ) - “log ratio”– 1 2x overexpressed– -1 2x underexpressed

cDNA plotted microarray

Microarray Expression Value Representation

expression value types

primary images composite imagese.g., green/red ratios

primaryspots

compositespots

primarymeasurements

derivedvalues

Source: MGED

Gene expression database – a conceptual view

SamplesG

Gene expression levels

Sample annotations

Gene annotations

Gene expression matrix

DAG Representation of Biomaterials

Sample sourcePrimary sample 1

Primary sample 2

Derived sample 1

Labeled extract 1

Extract 1

Derived sample 2

A new state ofsample source

Extract 2

Labeled extract 2Hybridizationlabeling

extraction

treatment

Source: MGED

ArrayExpress (MGED) Design

Experiment

e.g., publication, webresource

Reference

e.g., organismtaxonomy

Ontology

Sample

e.g., gene inSWISS-prot

Database

Hybridization

ExpressionValue

ArrayExpress

External links

Source: MGED

ArrayExpress (MGED) Architecture

data submission & Curation database

data warehouse

application serverWeb server

image server?

ArrayExpress

Curation pipeline

MAMLdata

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

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

3. Data Analysis (Clustering)

• Normalization

• Hierarchical Clustering

• Divisive Clustering

• Other Methods

• Visual Tools

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

Clustering

• Hierarchical – Single, Complete and Average Linkage

• Divisive– K-means

– Self Organizing Maps (SOM)

• Others– Principal Component Analysis (PCA)

– Supervised Methods

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

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

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

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)

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)

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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