DNA MicroarrayDNA Microarray

Microarray Printing

96-well-plate (PCR Products)

384-well print-plate

Microarray

Differential Expression

• Each cell contains a complete copy of the organism’s genome

• Cells are of many different types and state

e.g. blood, nerve, skin cells, etc• What makes the cells different ?• Differential gene expression, i.e., when, where

and in what quantity each gene is expressed• On average, 40% of our genes are expressed at

any given time

Functional genomics

• The various genome projects have yielded the complete DNA sequences of many organisms.

e.g. human, mouse, yeast, fruitfly, etc.• Human: 3 billion base-pairs, 30-40 thousand gen

es.• Challenge: go from sequence to function,

i.e., define the role of each gene and understand how the genome functions as a whole.

Central Dogma

• The expression of the genetic information stored in the DNA Molecule occurs in two stages:

--transcription, during which DNA is transcribed into mRNA;

--translation, during which mRNA is translated to produce a protein.

• DNA mRNA Protein

cDNAArrays

TissueArrays

The Central Dogma of Molecular Biology

Microarray Hybridization

MicroarrayGene

ExpressionImage

ABetterLook

200 10000 50.00 5.644800 4800 1.00 0.009000 300 0.03 -4.91

Cy3 Cy5Cy5Cy3

Cy5Cy3log2

Gen

es

Experiments842

fold248

Underexpressed

Overexpressed

Image Analysis & Data Visualization

New Data ScanAlyze/GenePix

Database

Data Selection

Complete Data Table (cdt)

Cluster

SOM

K-means

SVD

GenePix Pro 3.0.lnk

SpotList

Ovarian Tumor StudyM. Schaner

Samples that shouldCluster together do not

Data Normalization

Pool of Cell Lines Tumor

Different amounts of starting material.

Different amounts of RNA in each channel

Differential labeling efficiency of dyes

Differential efficiency of hybridization over slide surface

Differential efficiency of scanning in each channel.

Such biases have consequences:

• Plotting the frequency of un-normalized intensities reveals the differential effect between the two c hannels.

How do we deal with this?

Normalization: In general, an assumption is made that the averag

e gene does not change. You must understand your experiment and data to

judge whether that assumption is a good one. Usually true for gene expression experiments, but

not necessarily for aCGH or chromatin IP. Generally true for large arrays, but not for small " b

outique" arrays.

• Data may have an intensity-dependent

structure. • Plot log2(R/G) vs. log10(R*G) to reveal

this • Reveals : • variance in log ratios is greater at lower

intensities. • distribution may not be centered around

zero.

Normalization : The R-I Plot

R-I Plot, Raw DataR-I Plot Following Loess

Normalization: Loess

log10(R*G)

log2

(R/G

)

Cluster Analysis

• Cell Cycle example( Spellman 1988)

Overview of the Cell Cycle

• Purpose: – To create two new cells by dividing one

original cell

Cell Cycle: Key Concepts

– All parts of original cell must be replicated and split between new cells

– Each step must occur in precise manner and timing for successful cycle, and is strictly regulated

– mRNA and proteins for cell cycle genes are found at varying levels at different points of the cycle

– Mutations causing malfunction in regulation can result in cancer

Yeast Cell Cycle

Cell Cycle: Basic Description

http://www.bmb.psu.edu/courses/biotc489/notes/cycle.jpg

Cells grow out of synchrony.