Microarray Data Analysis III

Large Scale Gene Expression Data Analysis III

Yan Cui (ycui2@uthsc.edu)

The address of this webpage:

http://compbio.uthsc.edu/microarray/lecture3.htm

Algorithms for large scale gene expression data analysis

1) Hierarchical clustering (output: gene clusters)

2) K-means/medians clustering (output: gene clusters)

3) T-test (output: a group of differentially expressed genes)

4) SAM (output: a group of differentially expressed genes)

5) PTM (output: a group of genes matching the expression template)

What to do with these lists of selected genes?

How to understand the biological meaning of your gene list?

Start from the existing knowledge.

First, find out what are already known about those genes.

What is Ontology?

Ontology is the philosophical study of the nature of being, becoming, existence, or reality, as well as the basic categories of being and their relations. (from Wikipedia)

In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. (from Wikipedia)

Gene Ontology: A system of controlled vocabulary to describe gene functions and their relations

Making biological knowledge computable

Gene Ontology does not provide new biological knowledge, but it is a new way of representing and organizing existing biological knowledge. It is a standardized and computer-readable gene function classification system.

Gene Ontology has a hierarchical structure, which is a Directed Acyclic Graph (DAG).

Directed: A parent node and a child node are linked by a directed edge (arrow) starting from the parent node and ending at the child node. The parent node represents a broader functional category while the child node represents a more specific functional category.

Acyclic: No directed cycle. A node cannot be the descendant of itself.

Unified Gene Function Classification System for All Organisms

The number of classes in the three Ontologies

(from Dameron O, Bettembourg C, Le Meur N (2013) Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study. PLoS ONE 8: e75993)

Gene Ontology

Computational tools for Gene Ontology Analysis

Help you understand the gene lists from large scale gene expression analysis.

Link results from new data to existing knowledge

Gene list

Potential markers for the prognosis of breast cancer, a set of genes differentially expressed between two groups of breast cancer patients with significantly different five year survival after surgery and chemotherapy.

NCBI Entrez Gene ID

1942 1956 2064 2625 3002 4485 4602 4605 4609 5327 6659 6662 6722 7494

Gene Symbols

EFNA1 EGFR ERBB2 GATA3 GZMB MST1 MYB MYBL2 MYC PLAT SOX4 SOX9 SRF XBP1

DAVID Functional Annotation Tool

DAVID: Database for Annotation, Visualization, and Integrated Discovery, a web-based tool to perform functional analysis of lists of genes derived from genomic studies.

The wide-range collection of heterogeneous functional annotations in the DAVID Knowledgebase

Form Huang DW et al. (2007) DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35: W169-75.

Statistical models of enrichment (or over-representation)

	Selected genes	Non-selected genes
Having function F	N₁	N₂
Not having function F	N₃	N₄

1. Fisher Exact Probability

The Fisher exact probability for enrichment is calculated using the Gaussian hypergeometric distribution:

A small Fisher exact probability (e.g. <0.05) means the association between the gene list and function F is statistically significant, therefore, function F is a biological theme of your gene list.

2. EASE Score (Adjusted Fisher Exact Probability)

The EASE score is a conservative adjustment to the Fisher exact probability. It weights significance in favor of the association supported by more genes.

For example, (Douglas A Hosack, et al. Identifying biological themes within lists of genes with EASE. Genome Biology 2003, 4:R70).

206 genes is selected from a microarray of 13,679 genes,

Only one gene in the microarray belongs to a function category, X,

And that gene happens to appear on the list of the 206 genes,

Fisher Exact Probability is significant (p = 0.015).

A larger function category, Y, with 787 members in the microarray,

20 members on the list of 206 genes,

Fisher exact probability is also significant (p = 0.015).

A biological theme based on the presence of a single gene is not stable and is rarely interesting.

If the single gene happens to be a false positive, then the significance of the biological theme is entirely false.

The EASE score is calculated by removing one gene within category X from the list and calculating the resulting Fisher exact probability for that category,

The EASE score: p = 1 for category X and p = 0.027 for category Y,

Thus the EASE score eliminates the significance of the 'unstable' category X while only slightly affecting the significance of the more global theme Y.

The EASE score favors more robust biological themes of the gene list.

Functional Annotation Clustering

Similar function categories are grouped together

Form Huang DW et al. (2007) DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35: W169-75.

The kappa value represents the degree of similarity between two binary strings.

What else can you do with the DAVID Knowledgebase?

Form Huang DW et al. (2007) DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35: W169-75.

What do we know about the relationships between the genes?

GeneMANIA uses functional association data to connect genes to form a network.

Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity.

Large Scale Gene Expression Data Analysis III

Yan Cui (ycui2@uthsc.edu)

http://compbio.uthsc.edu/microarray/lecture3.htm

Further Reading