Module 10: Microarray Data Analysis I

Yan Cui (ycui2@utmem.edu)

The address of this webpage:

http://compbio.utmem.edu/MSCI814/Module10.htm or

http://compbio1.utmem.edu/MSCI814/Module10.htm

What is Microarray?

Microarray is array of DNA molecules that permit many hybridization experiments to be performed in parallel. It can monitor expression levels of thousands of genes simultaneously.

Microarray has become a powerful tool for biomedical research. The number of published papers referring to microarray has been growing exponentially in recent years.

Microarray Data Matrix

In analysis of microarray data, what we analyze is an expression matrix. Each column represents all the gene expression levels from a single experiment, and each row represents the expression of a gene across all experiments. Each element is a log ratio. The log ratio is defined as log₂ (T/R), where T is the gene expression level in the testing sample, R is the gene expression level in the reference sample.

The expression matrix can be presented as a matrix of colored rectangles. Each rectangle represents an element of the expression matrix.

(Adapted from documentation of MeV, http://www.tigr.org/software/tm4/mev.html)

Hierarchical Clustering

Hierarchical Clustering is the most popular method for microarray data analysis. In hierarchical clustering, the genes are connected iteratively based on their similarity. The genes with similar expression patterns are grouped together and are connected by a series of “branches”, which is called dendrogram (or clustering tree). With the same method, experiments with similar expression profiles can also be grouped together.

(Adapted from the documentation of MeV, http://www.tigr.org/software/tm4/mev.html)

The two fundamental problems in hierarchical clustering are:

1. How to determine the similarity between two genes?

2. How to determine the similarity between two clusters?

To solve the first problem, we calculate the distance between two expression vectors. The distance is used as a measure of similarity between genes.

Gene Expression Vector consists of the expression of a gene over a set of experimental conditions.

(From the documentation of MeV, http://www.tigr.org/software/tm4/mev.html)

The second problem is: How to determine the similarity between clusters? The method for determining cluster-to-cluster distance is called linkage method.

Three linkage methods:

(Adapted from the documentation of MeV, http://www.tigr.org/software/tm4/mev.html)

There is no guideline for selecting the best linkage method. In practice, people almost always use average linkage.

K-Means / K-Medians Clustering

What is Mean? What is Median?

Mean is what is commonly called the average.

Median is the “middle number”, i.e. the middle of the distribution. When there is an odd number of numbers, the median is simply the middle number. For example, the median of 2, 4 and 7 is 4.

When there is an even number of numbers, the median is the mean of the two middle numbers. Thus, the median of the numbers 2, 4, 7, 12 is (4+7)/2 = 5.5.

Median is more robust against outliers

For example, the mean of 5, 6, 7, 8 and 9 is 7. The median of 5, 6, 7, 8 and 9 is also 7.

But, the mean of 5, 6, 7, 8 and 99 is 25, while the median of 5, 6, 7, 8 and 99 is still 7.

(From the documentation of MeV, http://www.tigr.org/software/tm4/mev.html)

TIGR MultiExperiment Viewer

TIGR MultiExperiment Viewer (MeV) is a software for microarray data analysis. MeV is developed by a group of people at TIGR (The Institute for Genomic Research) and is freely available through TIGR web site. Like Perl, Mev uses The Artistic License, which means you can freely download the software or get a copy from another user (for details see http://www.tigr.org/software/tm4/generalFAQ.html).

Running MeV requires Java Runtime Environment (JRE). A detailed instruction on installation of JRE and MeV can be found at http://compbio.utmem.edu/MSCI814/faq.php.

Starting TIGR MultiExperiment Viewer

Double click the batch file (TMEV.bat) to start the program. Use the File menu to open a new Multiple Array Viewer.

Loading Microarray Data

Select Load data from the File menu to launch the file-loading dialog. At the top of this dialog, use the drop-down menu to select the type of expression files to load. Use the file browser to locate the files to be loaded.

HCL: Hierarchical clustering

Type of the Input file: Stanford Files (*.txt)

Name of the Input file: Stanford_Large.txt

Select Options:

1. Average Linkage;

2. Cluster both genes and experiments.

Working with clusters

Clusters of interest can be stored:

1. Click the dendrogram to select the cluster;

2. Open a menu by right clicking in the viewer and selecting the store cluster option;

3. Input the name of the cluster and select a color to label the cluster.

A color bar is displayed along the right side of cluster.

KMC: K-Means / K-Medians Clustering

Options:

1. Cluster genes;

2. Use mean;

3. Number of clusters = 5;

4. Number of iterations = 50;

Further Reading

1. The review articles in a special issue of Nature Genetics (http://www.nature.com/cgi-taf/DynaPage.taf?file=/ng/journal/v32/n4s/index.html)

2. Shannon W, Culverhouse R, Duncan J (2003) Analyzing microarray data using cluster analysis. Pharmacogenomics 4:41-51. (http://ilya.wustl.edu/~shannon/pharmacogenomics.pdf)

Homework

Due Date: Wednesday, March 24. Submit to Dr. Yan Cui via email (ycui2@utmem.edu). The solution will be posted on March 25 at http://compbio.utmem.edu/MSCI814/Solution1.htm.

Background: DNA microarray has shown great promise in studying complex diseases such as cancer. The genome-wide gene expression profiles of tumor tissues are considered as the “molecular portraits” of various cancers. For example, Clustering of breast and ovarian carcinoma cases is shown in the figure below. 68 breast and 57 ovarian cases were co-clustered to discern both similarities and disparities between the two sample sets. (Schaner, M et al., Gene Expression Patterns in Ovarian Carcinomas, Mol Biol Cell. 2003 Nov;14(11):4376-86).

Data: Download the dataset from http://compbio.utmem.edu/MSCI814/Homework1.txt (Right click on the link and select “Save Target As…”). The dataset contains gene expression profiles of 16 tumor samples. The 16 tumor samples belong to two types of cancer respectively.

1. Analyze the data with hierarchical clustering (HCL)

You should use average linkage method, Euclidean distance metric and only cluster experiments.

Please infer from the dendrogram (clustering tree) which samples belong to same type of cancer.

For example,

Cancer 1: Sample 1, 3,5,7,9,11,13,15

Cancer 2: Sample 2,4,6,8,10,12,14,16

2. Analyze the data with K-means clustering (KMC)

Use K-means clustering method to group the 16 samples into two clusters.

For example,

Cluster 1: Sample 1, 3,5,7,9,11,13,15

Cluster 2: Sample 2,4,6,8,10,12,14,16

What should be included in the email:

1. HCL

Cancer 1: Sample….

Cancer 2: Sample….

2. KMC

Cluster 1: Sample….

Cluster 2: Sample….