Nuria Lopez-Bigas

Methods and tools in functional genomics

(microarrays)

BCO17

What are microarrays?

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 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

Microarray data analysis

Microarray data analysis

Differential expression

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

Microarray data analysis

Differential expression

GO,KEGG…analysis

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

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

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/

Microarray data analysis

Differential expression

GO,KEGG…analysis

Classification

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

Classification

Support vectors machines

Desition trees

Microarray data analysis

Differential expression

GO,KEGG…analysis

Classification

Clustering

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

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

Microarray data analysis

Differential expression

GO,KEGG…analysis

Clustering

Classification

Promoter analysis

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

Promoter analysis::TFBS

TRANSFAC

Promoter analysis::Tools

http://www.cisreg.ca/

Microarray data analysis

Differential expression

GO,KEGG…analysis

Clustering

Classification

Promoter analysis

Reverse engineering

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis

Reverse engineering

Microarray data analysis

Differential expression

GO,KEGG…analysis

Clustering

Classification

Promoter analysis

Reverse engineering

Microarray DATANormalized data Data preprocession and normalization

Data

analy

sis