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Microarray Experiment Design and Data Interpretation. Susan Hester, Ph.D. Environmental Carcinogenesis Division Toxicogenomic Core Facility US EPA hester.susan@epa.gov 919-541-1320. Presentation Outline.

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slide1

Microarray Experiment Design

and Data Interpretation

Susan Hester, Ph.D.

Environmental Carcinogenesis Division

Toxicogenomic Core Facility

US EPA

hester.susan@epa.gov

919-541-1320

slide2

Presentation Outline

  • Traditional biology versus genomics
  • Basics of genomics
  • Data mining goals and approaches using parallel analyses
      • -some examples
  • Interpreting changes in gene expression to identify altered molecular pathways
  • Evaluating pathway alterations in concert with traditional toxicology data for greater understanding of mode of action
slide3

Traditional Biology

Measure one tree

at a time

Measure one element

in 10-50 samples

slide4

“Omic” Biology

Measure tens of

thousands of elements

in 2 to 4 samples

Measure Forests

(groups of trees)

slide5

Genomic research is a data-rich technology

  • Microarrays are called chips or arrays
  • Takes advantage of the natural property of DNA to pair with its complimentary strand
  • One strand is built into the array and then is used as a probe for the complementary strand in the biologic sample
  • The binding confirms the presence of mRNA or cDNA
  • In the sample
slide6

Genomic Profiling-Find ”Significantly

Changed Genes”

From:

All probesets

Typical experiment

is ~ 1M datapoints

To:

Reduce to a much

smaller number

of “meaningful genes”

slide7

Finding genes in samples-1st step

1 genechip cell location

1 genechip

apply sample

slide8

2nd step

Tagged DNA fragments that

base pair will glow

2nd step

shine light

final image

text file with

gene intensities

experimental design
Experimental Design
  • Use adequate controls
  • Sample collection
  • Choose time-points and doses
  • Hybridization schemes-1 or 2 colors
slide10

Data Quality and Data Mining

  • RNA quality
  • Scans
  • Summary statistics
slide11

RNA quality:

  • Agilent 2100 Bioanalyzer
  • Measure RNA quality and quantity
  • Uses small sample size and take minutes

Good

Quality

RNA

Degraded

RNA

Agilent Gel Image

slide12

QC Assessment of Scanned Slide

  • Showing Good
  • Dynamic Range
  • of Signal Intensity
  • Low background
  • signal

Poor scan

Good scan

slide13

Summary Statistics for each array

Raw gene intensity distribution

for each array

After normalization shows

reduced variance

max

median

min

Grp 1 2 3 4 5 6

slide14

Example of with-in group outliers

Example of 2 array outliers

(high and low median values)

Arrays

slide15

Goals of Data Mining

  • Reduce the large dataset by first exclude “unchanging
  • genes”
  • Early microarray papers used a simple “fold change”
  • to find differences
  • Most analyses now rely on statistical tests to identify
  • changed genes-supervised versus unsupervised
  • Find genes that distinguish the various biologic classes
  • “significant genes”
slide16

General Approach: From many genes to a few

28,000 rat genes

34,000 mouse genes

normalize data to compare across arrays

analysis begins here

supervised (prior knowledge) and unsupervised (no prior knowledge)

T test, ANOVA, etc. PCA, KNN, clustering

genes…now associate with gene name

using databases to assign gene function

characterize genes into pathways

explore pathways by combining into networks

slide17

Array Image Inspection Confirms the

Induction of Many Genes

1 uM As50 uM As

slide18

Statistical Filter shows more

significant genes at higher doses

1 uM As

50 uM As

genes that have values>1.5 fold

and significant

p<0.05

slide19

Many Views of the Data

  • Table of filtered genes
  • Principal Component Analysis (PCA)
  • Venn Diagrams-gene level
  • Correlate Transcription with Functional Assays
  • Map genes to pathways
  • Venn Diagram-pathway level
slide20

Table view:

Significantly Altered Genes by Chemical, Day and Dose

in rat liver

slide21

Principal Component Analysis

  • Identifies dose-response, if present
  • Assess experiment
    • Worth analyzing ?
  • Identify outliers-bad chips
    • Find samples with similar expression patterns

What it does

What it looks like:

  • uses all samples and genes
  • using statistics, reduces and plots the data
  • helps visualize data in 2 or 3 planes (3D)

What it tells

  • groups samples or genes with similar profiles
  • differentiates treatment or exposure groups
slide24

Dose response corresponds to

functional assays

Functional assays

Better description of dose response by genomics

slide26

Pathway Venn

Unique and common pathways over time

slide27

Pathway and network visualizations

  • cellular
  • molecular
  • network
  • metabolic
  • transcription
slide28

Example of a molecular pathway with

gene intensity values added

Oxidative Phosphorylation pathway

red=gene induced

green=gene repressed

rainbow=mixed

ATPase

Oxidoreductase

NADH dehydrogenase

succinate dehydrogenase

complex

cytochrome c

oxidase subunit

slide29

Cellular pathway

extracellular

cytoplasmic

Note c-Jun

JNK1, ERK1

repression*

nuclear

Expression legend

Green= decreased

Red=increased

Rainbow=mixed

slide30

Gene Network:

One Transcription factor:

slide32

Conclusions

  • Steps for a successful microarray experiment:
  • Experiment design-focus your research question
  • Data quality assessment
  • Supervised and unsupervised analyses
  • Integrating gene expression results with other
  • phenotypic endpoints