dr paul lewis lecturer in bioinformatics lecturer in bioinformatics cardiff university cardiff...

Dr Paul LewisDr Paul Lewis

• Lecturer in BioinformaticsLecturer in Bioinformatics

• Cardiff UniversityCardiff University

• Biostatistics & Bioinformatics UnitBiostatistics & Bioinformatics Unit

Biostatistics & Bioinformatics Unit(BBU)

• Bioinformatics resource for Institutions across Wales

• Backing of the Higher Education Funding Council for Wales - £1.5 million grant through the Research Capacity Development Fund

• UWCM, Cardiff University, Aberystwyth

• 13 new posts in statistics & bioinformatics

• MSc/Postgraduate Diploma/Postgraduate Certificate:

Bioinformatics

Genetic Epidemiology and Bioinformatics

• Brief Overview of Microarray Bioinformatics

• Introduce My Microarray Research Interests

• My Microarray Analysis Software

Experimental Experimental DesignDesign

Differential Differential Gene Gene ExpressionExpression

HybridisationHybridisation

DataData

Pattern Pattern DiscoveryDiscovery

Class Class PredictionPrediction

AnnotationAnnotation

NormalisationNormalisation

Bioinformatics in Microarray ExperimentBioinformatics in Microarray Experiment

NormalizationNormalization

Remove non-biological influences on data (systematic variation)

3 categories of Normalisation

• Normalisation – transform data to make more like a normal distributionlog, lowess, linlog

• Standardisation – expand or contract distribution so data from different experiments can be compared

calculate Z-scores

• Centralisation – move distribution so its centered around expected meanmean / median / mean trimmed centering




DataData






With Replicates

Parametric tests

t-test (ANOVA) J. Comput. Biol. 2000 7: 817-838

Bayesian t-test Bioinformatics 2001 17: 509-519.

Mixture modelling & bootstrapping (SAM) P.N.A.S. 2001 98: 5116-5121

Regression modelling Genome Res. 2001 11: 1227-1236.

All give similar results but SAM reduces false positives

Non Parametric Tests

Wilcoxon rank sum test Bioinformatics 2002 18: 1454-1461

Non-parametric t-test Bioinformatics 2002 18: 1454-1461

Ideal discriminator method Bioinformatics 2002 18: 1454-1461

low false positive rate but less power

Find Differentially Expressed GenesFind Differentially Expressed GenesIs fold change significant?Is fold change significant?




DataData






Pattern Discovery & Class PredictionPattern Discovery & Class Prediction

Explore how genes or samples group:

Clustering

Hierarchical Cluster Analysis HIERARCHYK-MeansSelf Organising Maps (SOM) PARTITIONFuzzy ARTPrincipal Components Analysis (PCA)Multidimensional Scaling (MDS) REDUCTIONCorrespondence Analysis (CoA)

Assign genes to known groupings:

Classification

logistic regressionneural networkslinear discriminant analysis

Hierarchical Cluster AnalysisHierarchical Cluster Analysis

Partitioning Clustering MethodsPartitioning Clustering Methods

• Need To Tell Methods Number of Clusters

• Genes Partitioned into Clusters

• What are Relationships Between Clusters?

K-Means & SOM

2D & 3D Mapping Methods2D & 3D Mapping Methods

CoAMDS

PCA

Data Projected onto 2 or 3 Dimensions

But….What are ClusterBoundaries?




DataData






Online Tools:

ARROGANT http://lethargy.swmed.edu/

DAVID http://apps1.niaid.nih.gov/david/

DRAGON http://207.123.190.10/dragon.htm

EASE http://apps1.niaid.nih.gov/david/

FANTOM http://www.gsc.riken.go.jp/e/FANTOM/

GoMiner http://discover.nci.nih.gov/gominer/

MatchMiner http://discover.nci.nih.gov/matchminer/

Onto-Express http://vortex.cs.wayne.edu/Projects.html

RESOURCERER http://pga.tigr.org/tigr-scripts/magic/r1.pl

Affymetrix GO http://www.affymetrix.com

Databases:

Gene Ontology http://www.geneontology.org/

OMIM http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM

LocusLink http://www.ncbi.nlm.nih.gov/LocusLink/

UniGene http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM

LocusLink http://www.ncbi.nlm.nih.gov/LocusLink/


My Research InterestsMy Research Interests

Pattern Discovery

Algorithm Development

Biologist-Friendly Software Tools

Take - 2D & 3D Mapping Methods

Methods - Define Cluster Boundaries

Make FUZZY

EAS-I

2D & 3D Visualisation Tools

Cluster BoundariesCluster Boundaries

CoAMDS

PCA

Fuzzy ClusteringFuzzy Clustering

• Differs to standard clust by assigning membership of a gene to all clusters

• Allows you to see the association of each gene within a cluster

• Can calculate the number of clusters in Partitioning methods (Fuzzy ART)

• Helps Combine Clusters

• Helps to clear Ambiguity

Fuzzy MappingFuzzy Mapping

Add Membership values of each gene to clusters

Fuzzy PartitioningFuzzy Partitioning

K-Means & SOM

• Need for Comprehensive Pattern Discovery Software Suite

• Fuzzy Data Analysis Suite

• Visualisation Tools to explore data

• Easy to use

• Free

Microarray Pattern Discovery

BBUnit

• Web based version

• Service by BBU

• Increase traffic to BBU web site

• Establish BBU for microarray

• Cross platform

INTERFACE

Normalisation DifferentialGene

Expression

Pattern Discovery

Utilities

•Log•Normalise•Mean Centre•Median centre

•T test•ANOVA•Regression

•Hierarchical Cluster Analysis•SOM•K-Means•Fuzzy Art•PCA•MDS•CoA•Fuzzy C-Means

http://bbu.uwcm.ac.uk

[email protected]

ContactContact

• Pete Kille• Alan Clarke• Gareth Hughes (EASI team)• Karen Reed (Data)• Lesley Jones (Data, & EASI Collaborator)• BBU

AcknowledgementsAcknowledgements

dr paul lewis lecturer in bioinformatics lecturer in bioinformatics cardiff university cardiff...

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