Nuria Lopez-Bigas

Nuria Lopez-Bigas

Methods and tools in functional genomics

(microarrays)

BCO17

What are microarrays?

Microarray data analysis is the step that will allow us to extract biological meaning to high-throughput data generated with the experiment.

Microarray data analysis


Microarray DATANormalized data Data preprocession and normalization

Normalization and Noise:

Normalization

• Some kind of normalization is usually required when comparing more

than one microarray experiment.

• Adjust to account for differences in overall brightness of slides

• Normalize relative to housekeeping genes

Noise

• Refers to variability and reproducibility of microarray experiments

• Intra and inter-microarray variations can significantly skew

interpretation of data

• Sample collection is very important. If comparing two conditions you

must control for all variables other than the one you are trying to measure

• Technical noise can result from imperfections in the chip.

• Both biological and technical replicates are required to measure and

control these sources of noise



Differential expression


Data

analy

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GO,KEGG…analysis


Data

analy

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http://www.geneontology.org

The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism.

The Ontologies •Cellular component•Biological process•Molecular function

BROWSER::AMIGO

TOOLS

Gene Ontology

Gene Ontology

Gene Ontology::Tools

http://www.geneontology.org/GO.tools.shtml

http://www.fatigo.org/

http://www.barleybase.org/funcexpression.php

http://discover.nci.nih.gov/gominer/htgm.jsp

FUNC-EXPRESSION

KEGG http://www.genome.jp/kegg/



GO,KEGG…analysis

Classification


Data

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Classification

Support vectors machines

Desition trees



GO,KEGG…analysis

Classification

Clustering


Data

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Supervised versus Unsupervised:

Supervised

• Analysis to determine genes that fit a predetermined pattern

• Usually used to find genes with expression levels that are significantly different between

groups of samples or finding genes that accurately predict a characteristic of the sample

• Two popular supervised techniques would be nearest-neighbour analysis and support

vector machines.

Unsupervised

• Analysis to characterize the components of a data set without a priori input or

knowledge of a training signal

• Try to find internal structure or relationships in data without trying to predict some

‘correct answer’.

• Three classes:

1. Feature determination: Look for genes with interesting patterns

Eg. Principal-components analysis

2. Cluster determination: Determine groups of genes with similar expression patterns

eg. Nearest-neighbour clustering, self-organizing maps, k-means clustering, 2d

hierarchical clustering

3. Network determination: Determine graphs representing gene-gene or gene-phenotype

interactions.

Eg. Boolean networks, Bayesian networks, relevance networks

Clustering & Classification

Clustering & Classification

Cooper Breast Cancer Res 2001 3:158



GO,KEGG…analysis

Clustering

Classification

Promoter analysis


Data

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Promoter analysis::TFBS

TRANSFAC

Promoter analysis::Tools

http://www.cisreg.ca/



GO,KEGG…analysis

Clustering

Classification

Promoter analysis

Reverse engineering


Data

analy

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Reverse engineering



GO,KEGG…analysis

Clustering

Classification

Promoter analysis

Reverse engineering


Data

analy

sis

Nuria Lopez-Bigas

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