Microarray Data Analysis III

Yan Cui (ycui2@uthsc.edu)

The address of this webpage:

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

Algorithms for microarray data analysis

1) Hierarchical clustering (output: gene clusters and sample clusters)

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

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

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

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 known about the genes in the list.

GO provides no 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.

GO 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.

Gene Ontology: Unified Gene Function Classification System for All Eukaryotes

Web-based Computational Tools for Gene Ontology -- Bridging the Gap between Microarray Data and Existing Biological Knowledge

Help you understand the gene lists from microarray analysis.

Gene Ontology

Tools for GO Analysis

GeneInfoViz

The GeneInfo database

1. GeneInfoViz includes parser programs that can download and parse gene information from public databases such as UniGene, LocusLink and Gene Ontology.

2. The compiled gene information is then saved in our local database -- GeneInfo.

3. Currently, the GeneInfo database contains function information of genes of human, mouse, rat, fruit fly, and C. Elegan etc. More species will be added in the future.

Batch retrieval of gene information

1. The input is a list of query genes.

2. Search by LocusLink IDs, UniGene IDs, and official gene symbols.

3. Select evidence codes.

4. The output includes a short description of gene function and Gene Ontology terms.

5. There are three types of GO terms: biological process [P], molecular function [F], and cellular component [C].

6. Associations of genes with GO terms are tagged with an evidence code that categorizes the source of the annotation, e.g. electronic annotation, experimental evidence. Users can choose to exclude the associations between genes and GO tagged with undesired evidence codes. By default, the associations with any evidence code are included.

UPLOAD 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 chemotherapy.

The Gene List

NCBI Entrez Gene ID (or LocusLink 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

The directed acyclic graphs of Gene Ontology

1. The GO system has a hierarchical structure, the broader biological roles are at the higher levels, and more specific roles are at the lower levels.

2. A DAG (Directed Acyclic Graph) is used to display the hierarchical structure of Gene Ontology.

3. Each node represents a Gene Ontology term.

4. 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.

5. GeneInfoViz filters the GO DAG and only shows the part that is associated with the query genes.

DAG

Construction and dynamic visualization of gene relation networks

Given all the information about the genes in the databases, what can we say about the relationships between the genes?

For example, we want to know if the genes are related and how closely they are related based on the existing knowledge about their biological roles.

1. The results of batch query include the GO terms that are associated with each gene.

2. GeneInfoViz starts from these GO terms and traces the paths up to a higher level (determined by user) in the GO DAG.

3. An indicator table is used to code the genes’ biological roles. Only 1 and 0 occur in the indicator table: "1" means the gene is associated with the biological role, "0" means it is not.

4. Based on the indicator table, we can construct an adjacency matrix. The distances between genes are determined by the adjacency matrix. Basically, the more biological roles the two genes share, the closer they are.

5. GeneInfoViz dynamically displays the gene relation network G using a Java Applet.

6. It makes a planar embedding of G in the two-dimensional space.

7. The program allows users to change the graph layout by clicking and dragging the vertices (genes). The program moves the vertices to keep the lengths of the edges in the network as close to L (the distance determined by the adjacency matrix) as possible.

Level 7

BP_7

Level 8

BP_8

DAVID Functional Annotation Tool

DAVID: Database for Annotation, Visualization, and Integrated Discovery, a web-based tool to provide functional interpretation 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.

UPLOAD GENE LIST

The Gene List

Entrez Gene ID (or LocusLink ID)

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

GENE_SYMBOL

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

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 probability 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 would be significant (p = 0.0152).

A more common function category, Y, with 787 members in the microarray,

20 members on the list of 206 genes,

Fisher exact probability would be slightly less significant (p = 0.0154).

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 dependent 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 for these two situations is p = 1 for category X and p < 0.0274 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 penalizes the significance of categories supported by fewer genes and thus favors more robust categories than the Fisher exact probability.

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.