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Microarray Design and Analysis. Jeremy D. Glasner. Genetics 875 November 20, 2007. What is a Microarray?. A collection of DNA sequences arrayed on a solid substrate usually with thousands of individual DNA spots. Gene expression analysis. Massively parallel biochemistry aimed

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Microarray Design and Analysis

Jeremy D. Glasner

Genetics 875

November 20, 2007


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What is a Microarray?

A collection of DNA sequences arrayed on a solid substrate

usually with thousands of individual DNA spots


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Gene expression analysis

Massively parallel biochemistry aimed

at measuring RNA levels


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Why do gene expression analysis?

  • Predict new gene functions by expression patterns

  • Determine the effect of a drug on gene expression

  • Compare a mutant strain to wild-type

  • Identify an expression signature for a cancer type

  • Find the targets of a transcriptional regulator

  • Measure the half-lives of RNAs in the cell

.

.

.



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TraSH: transposon site hybridization

Genome wide localization of insertion mutations

Sassetti CM, Boyd DH, Rubin EJ. Proc Natl Acad Sci U S A. 2001 98(22):12712-7


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CGH: Comparative genome hybridization

Rajashekara G, Glasner JD, Glover DA, Splitter GA. J Bacteriol. 2004 186(15):5040-51.


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ChIP-Chip: Chromatin immunoprecipitation, chip hybridization

a.k.a. genome-wide occupancy profiling

Identify the chromosomal locations of a DNA binding protein


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Flow of Information in Array Analyses

Experimental Design

Array Production

Sample Preparation

Scanning

Image Analysis

Data Processing

Data Analysis

Information Integration


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Experimental Design Issues

Number of Replicates

What samples should be compared?

Directly on same chip/across arrays?

Calibrators and common references

What controls are necessary?


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Considerations when designing the sequences for a chip

Sequence Annotation

ORF, UTR, functional RNA prediction

Oligo Selection

Array Design, Replication


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Sensitivity vs. specificity as a function of oligo length

They are inversely related

Hughes TR, et al., Nat Biotechnol 2001. Apr;19(4):342-7.


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Two methods for array production

photolithography

spotting


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Affymetrix Chips

105-106 “Probes”

Perfect Match and Mismatch

Average Difference Values


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Affymetrix “Units”

PM

MM

A “probe set”

A “probe pair”


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DMD “The Digital Light Switch”

DMD Close-Up

  • Mirrors spacing 17 um

  • Mirror transit time <20 us

  • Tilt angle +10 degrees

  • Five mirrors = diameter human hair

  • Analog pictures from digital switches?




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Sample Preparation

RNA samples are extracted for the experiment and fluorescent dyes are incorporated

RNA stabilization

Direct vs. indirect labeling


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Hybridization & Scanning

PMT settings

Lasers

Focus

Data Tracking


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Image Analysis

Spot Finding

Background subtraction

Intensity Calculation


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Automatic Grid Finding

Sum signal intensities in X and Y directions



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Estimating Foreground and Background with the “Histogram” Method

#

pixels

Intensity


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Quality Filtering of Data “Histogram” Method

From Tseng et al., 2001. NAR 29(12):2549-2557


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Data Normalization “Histogram” Method

Data Normalization is necessary if the overall signal differs between experiments and can be complicated if the relationship is nonlinear.

Internal controls can also be used for normalization.

Data from Schadt et al 2001. Journal of Cellular Biochemistry Supplement 37:120-125.


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M-A Plots “Histogram” Method


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Print Tip Group Normalization “Histogram” Method


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Intensity Dependent Normalization “Histogram” Method


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Normalization Methods “Histogram” Method

Assume linear relationship

Apply non-linear normalization

Normalize to “house-keeping genes”

Normalize to internal Standards


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Ratio Calculation Methods “Histogram” Method


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Detecting differential expression “Histogram” Method

Determine which changes are significant:

Fixed cutoff (fold-change>4)

Replication allows assessment of variability

Common statistics such as the t-test are often used for gene expression data. Significance of the value is then determined by referring to the t distribution. This assumes that the data is normally distributed, which may not be true.

Gene expression experiments may require thousands of statistical tests and significance should be adjusted to reflect this. A standard Bonferroni correction is the p-value multiplied by the number of tests but is likely too conservative.


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Different methods, different results “Histogram” Method

Millenaar et al., BMC Bioinformatics2006, 7:137


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