Large Scale Gene Expression Data Analysis II

Yan Cui (ycui2@uthsc.edu)

The address of this webpage:

http://compbio.uthsc.edu/microarray/lecture2.htm

 

Two Algorithms for Identifying Differentially Expressed Genes

o   T-Test

o   SAM: Significance Analysis of Microarray Data

 

Why is it important to study differential gene expression?

Each cell of an organism contains a complete copy of the organism's genome.

Cells are of many different types and states, e.g. blood, nerve, and skin cells, dividing cells, etc.

What makes the cells different?

Differential gene expression, i.e., when, where, and in what quantity each gene is expressed.

(Statistics for Microarray Data Analysis by Terry Speed)

 

The differentially expressed genes are the genes whose expression levels are significantly different between two groups of experiments (or samples).

 

3. Calculate the observed t-statistic for each gene

, where

Description: Description: Description: equations

The T statistic is the distance between the two groups in units of standard deviation.

 

4. Calculate probability value (p-value, between 0 and 1) of the t-statistic for each gene using t-distribution.

P=0.05

P=0.05

 

Summary:

Input: Expression matrix and grouping for experiments

Output: A p-value for each gene. Small p-value = differential expression.

 

SAM: Significance Analysis of Microarray Data

3. For each gene, compute d-value (analogous to t-statistic).

This is the observed d-value.

Theterm is here to deal with cases when the variance gets too close to zero (numerically stable).

(Adapted from SAM: Significance Analysis of Microarray Data by Paul Delmar)

4. Permutation: Randomly shuffle gene expression values between the two groups, such that the reshuffled groups A and B respectively have the same number of elements as the original groups A and B. Compute the d-value for each randomized grouping:

 

 

Repeat step 4 many times, so that each gene has many randomized d-values. Take the average of the randomized d-values for each gene. This is the expected d-value of that gene.

Why permutation? Destroy any differential expression between the two groups. This would allow us to estimate the d-value when this is no differential expression (i.e. the expected d-value).

 

The program plots the observed d-values vs. the expected d-values (horizontal axis = the expected d-values, vertical axis = observed d)

It is a very interactive algorithm allows users to dynamically change thresholds for significance (by tuning parameter delta) after looking at the distribution of the d-value.

(Adapted from documentation of MeV)

 

 

Perform T-test and SAM with MeV

Data File: Stanford_Large.txt

TTEST: T-test

Specify Options:

 

The Expression Image of the Significant Genes:

 

Click on the header of a column to sorting rows in ascending order based on the contents of the column

SAM: Significance Analysis of Microarray Data

 

Specify Options:

 

SAM Graph:

 

Significant Genes:


 

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. (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. (Wikipedia)

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

 

The purpose of Gene Ontology is to make 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

 

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

Link results from new data to existing knowledge

 

 

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.

 

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.

Gene Symbols

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 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.

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.

 

 

Further Reading

1.    Pan, W. (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 2002 18: 546-554.

2.    Cui, X. and Churchill, G.A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biology 4:210.

3.    Stockburger D.W., The t distribution, http://www.psychstat.missouristate.edu/introbook/sbk24.htm

4.    The Gene Ontology Consortium. (2013) Gene Ontology Annotations and Resources. Nucl. Acids Res. 41: D530-D535.

5.    Huang DW, et al. (2007) DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene list. Genome Biol. 8:R183.

6.    Huang, D. W., B. T. Sherman, and R. A. Lempicki. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4: 44-57.

 

Homework

Due Date: February 25. Submit to Dr. Yan Cui via email (ycui2@uthsc.edu).

Data: Download the dataset from http://compbio.uthsc.edu/MSCI814/Homework2.txt (Right click on the link and select Save Target As). The dataset contains gene expression profiles of 8 breast cancer samples (BC1-8) and 8 ovarian cancer samples (OC1-8).

1.    Identify the genes that are differentially expressed between the two groups (8 breast cancer samples vs. 8 ovarian cancer samples) using:

a.     T-test

o   How many significant genes are identified (using the default options)?

o   Which gene has the smallest p-value?

b.    SAM

o   Which gene has the largest (the absolute value of observed score (d))?

2.    Identify the genes that match to the template expression pattern using:

a.     PTM

o   Use the gene identified in question 2a (i.e. the gene with the largest in SAM analysis) as template. How many genes match to this template (set p=0.001)?

 

3.    Use DAVID to identify the major biological functions associated with the list of Affymetrix microarray probe-sets (mouse data). What is the most significant functional term (i.e. the term with the smallest p-value) for the gene list? How many genes in the gene list are annotated to this term? What is the enrichment score of the most significant annotation cluster?

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