Microarray Technology

Types

Normalization

Microarray Technology

• Microarray:– New Technology (first paper: 1995)

• Allows study of thousands of genes at same time

– Glass slide of DNA molecules • Molecule: string of bases (25 bp – 500 bp) • uniquely identifies gene or unit to be studied

http://kbrin.a-bldg.louisville.edu/CECS694/

Fabrications of Microarrays

• Size of a microscope slide

Images: http://www.affymetrix.com/

Differing Conditions

• Ultimate Goal:– Understand expression level of genes under

different conditions

• Helps to:– Determine genes involved in a disease– Pathways to a disease– Used as a screening tool

Gene Conditions

• Cell types (brain vs. liver)

• Developmental (fetal vs. adult)

• Response to stimulus

• Gene activity (wild vs. mutant)

• Disease states (healthy vs. diseased)

Expressed Genes

• Genes under a given condition– mRNA extracted from cells– mRNA labeled– Labeled mRNA is mRNA present in a given

condition– Labeled mRNA will hybridize (base pair) with

corresponding sequence on slide

Two Different Types of Microarrays

• Custom spotted arrays (up to 20,000 sequences)– cDNA– Oligonucleotide

• High-density (up to 100,000 sequences) synthetic oligonucleotide arrays– Affymetrix (25 bases)– SHOW AFFYMETRIX LAYOUT

Custom Arrays

• Mostly cDNA arrays

• 2-dye (2-channel)– RNA from two sources (cDNA created)

• Source 1: labeled with red dye• Source 2: labeled with green dye

Two Channel Microarrays

• Microarrays measure gene expression

• Two different samples:– Control (green label)– Sample (red label)

• Both are washed over the microarray– Hybridization occurs – Each spot is one of 4 colors

(Slide source: http://www.bsi.vt.edu/)

Microarray Image Analysis

• Microarrays detect gene interactions: 4 colors: – Green: high control– Red: High sample– Yellow: Equal– Black: None

• Problem is to quantify image signals

Information Extraction

—Spot Intensities—mean (pixel intensities).—median (pixel intensities).

—Background values—Local —Morphological opening—Constant (global)—None

—Quality Information

Take the average

Speed Group Microarray Page

http://stat-www.berkeley.edu/users/terry/zarray/Html/image.html

Signal

Background

Single Color Microarrays

• Prefabricated – Affymetrix (25mers)

• Custom– cDNA (500 bases or so)– Spotted oligos (70-80 bases)

Single Color Microarrays

• Expressed sequences washed over chips

• Expressed genes hybridize

• Light passed under to see intensity (or hybridized oligos show dark color)

Single Color Microarrays

Image: http://www4.nationalacademies.org/

Affymetrix Technology

DESOKY, 2003

Affymetrix Technology

DESOKY, 2003

Lithography

• It is a printing technology.• Lithography was invented by Alois

Senefelder in Germany in 1798.• The printing and non-printing areas of the

plate are all at the same level, as opposed to intaglio and relief processes in which the design is cut into the printing block.

• Lithography is based on the chemical repellence of oil and water.

Lithography

Designs are drawn or painted with greasy ink or crayons on specially prepared limestone. The stone is moistened with water, which the stone accepts in areas not covered by the crayon. An oily ink, applied with a roller, adheres only to the drawing and is repelled by the wet parts of the stone. The print is then made by pressing paper against the inked drawing.

Affymetrix Array Construction

STROMBERG, 2003

Affymetrix Technology

Biotin (one dye) instead of 2 colorsOne treatment per chip11, 16, or 20 gene markers pairs per gene

DESOKY, 2003

Affymetrix Data

• Each gene labeled as “present”, “marginal”, or “absent.” – Present: gene expressed and reliably

detected in the RNA sample

• Label chosen based on a p-value

PM to maximize hybridization

MM to ascertain the degree of cross-hybridization

Affymetrix Design of probes

PMMM

Probe set

Probe pair

STROMBERG, 2003

Inferential statistics

Paradigm Parametric test Nonparametric

Compare two unpaired groups Unpaired t-test Mann-Whitney test

Compare twopaired groups Paired t-test Wilcoxon test

Compare 3 or ANOVAmore groups

Inferential statistics

Is it appropriate to set the significance level to p < 0.05?If you hypothesize that a specific gene is up-regulated,you can set the probability value to 0.05.

You might measure the expression of 10,000 genes andhope that any of them are up- or down-regulated. Butyou can expect to see 5% (500 genes) regulated at thep < 0.05 level by chance alone. To account for thethousands of repeated measurements you are making,some researchers apply a Bonferroni correction.The level for statistical significance is divided by thenumber of measurements, e.g. the criterion becomes:

p < (0.05)/10,000 or p < 5 x 10-6

Data matrix(20 genes and 3 time pointsfrom Chu et al.)