dna microarray bioinformatics

DNA Microarray Bioinformatics - #27612

Normalization

Getting the numbers comparable


Sample PreparationHybridization

Array designProbe design

QuestionExperimental Design

Buy Chip/Array

Statistical AnalysisFit to Model (time series)

Expression IndexCalculation

Advanced Data AnalysisClustering PCA Classification Promoter AnalysisMeta analysis Survival analysis Regulatory Network

ComparableGene Expression Data

Normalization

Image analysis

The DNA Array Analysis Pipeline


Expression intensities are not just target concentrations

• Sample contamination

• RNA quality• Sample preparation• Dye effect (cy3/cy5)• Probe affinity• Hybridization• Unspecific signal

(background)• Saturation

•Spotting•Other issues related to array manufacturing

•Image segmentation•Array spatial effects


Gene-specific variation

Spotting (size and shape)Cross-hybridizationDye

Biological variation– Effect– Noise

Global variation

RNA qualitySample preparationDyeHybridizationPhotodetection

Systematic

Two kinds of variation in the signal

Stochastic


Gene-specific variation:

• Too random to be explicitly accounted for• “noise”

Global variation:

• Similar effect on many

measurements• Corrections can be estimated from data

Normalization Statistical testing

Sources of variation

Systematic Stochastic


Calibration = Normalization = Scaling


Nonlinear normalization


Lowess Normalization

One of the most commonly utilized normalization techniques is the LOcally Weighted Scatterplot Smoothing (LOWESS) algorithm.

M

A

* * * *** *


The Qspline method

From the empirical distribution, a number of quantiles are calculated for each of the channels to be normalized (one channel shown in red) and for the reference distribution (shown in black)A QQ-plot is made and a normalization curve is constructed by fitting a cubic spline functionAs reference one can use an artificial “median array” for a set of arrays or use a log-normal distribution, which is a good approximation.


Once again…qspline

When many microarrays are to be normalized to each other an average array can be used as target

Accumulating quantiles


QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Invariant set normalization (Li and Wong)

A invariant set of probes is used-Probes that does does not change intensity rank between arrays-A piecewise linear median line is calculated-This curve is used for normalization






Spatial biasestimate

Spatial normalization

After intensitynormalization

After spatialnormalization

Raw data After intensitynormalizationAfter intensitynormalization

After spatialnormalizationAfter spatial

normalization


Sample PreparationHybridization

Array designProbe design

QuestionExperimental Design

Buy Chip/Array

Statistical AnalysisFit to Model (time series)

Expression IndexCalculation

Advanced Data AnalysisClustering PCA Classification Promoter AnalysisMeta analysis Survival analysis Regulatory Network

ComparableGene Expression Data

Normalization

Image analysis

The DNA Array Analysis Pipeline


Expression index value

Some microarrays have multiple probes addressing the expression of the same target

– Affymetrix GeneChips have 11-20 probe pairs pr. Gene

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.- Perfect Match (PM)

- MisMatch (MM)

PM: CGATCAATTGCACTATGTCATTTCT MM: CGATCAATTGCAGTATGTCATTTCT


Expression index calculation

Simplest method?Median

But more sophisticated methods exists:dChip, RMA and MAS 5


dChip (Li & Wong)

Model: PMij = ij + ij

Outlier removal:– Identify extreme residuals– Remove– Re-fit– Iterate

Distribution of errors ij assumed independent of signal strength

(Li and Wong, 2001)


RMA

Robust Multi-array Average (RMA) expression measure (Irizarry et al., Biostatistics, 2003)

For each probe set, re-write PMij = ij as:

log(PMij)= log(i ) + log(j)

Fit this additive model by iteratively re-weighted least-squares or median polish


MAS. 5

MicroArray Suite version 5 uses

MM* is an adjusted MM that is never bigger than PM

Tukey biweight is a robust average procedure with

weights and outlier rejection

)}{log( *jj MMPMghtTukeyBiweisignal −=


Std Dev of gene measures from 20 replicate arrays

Methods compared on expression variance

Standard deviation of gene measures from 20 replicate arrays

RMA: Blue and RedMAS5: GreendChip: Black

Expression level

From Terry speed


Robustness

MAS5.0

(Irizarry et al., Biostatistics, 2003)



MAS 5.0

Log fold change estimate from 1.25ug cRNA

Log

fold

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Robustness

dChip


dChip


Log

fold

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Robustness

RMA


RMA


Log

fold

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All of this is implemented in…

R

In the BioConductor packages ‘affy’

(Gautier et al., 2003).


References

Li and Wong, (2001). Model-based analysis of oligonucleotide arrays: Model validation, design issues and standard error application. Genome Biology 2:1–11.

Irizarry, Bolstad, Collin, Cope, Hobbs and Speed, (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 31(4):e15.)

Affymetrix. Affymetrix Microarray Suite User Guide. Affymetrix, Santa

Clara, CA, version 5 edition, 2001.

Gautier, Cope, Bolstad, and Irizarry, (2003). affy - an r package for the analysis of affymetrix genechip data at the probe level. Bioinformatics

dna microarray bioinformatics - #27612 normalization getting the numbers comparable

Documents

spatial normalization

nonlinear normalization

lowess normalization

normalization curve

scaling slide

invariant set normalization

numbers comparable slide