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cDNA microarrays

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cDNA microarrays• small slides with several

measurement units, spots • e.g. 2.5cm-by-7.6cm glass

slide with 30,000 spots• each spot contains specific

nucleotide sequences, probes• in hybridization process,

labeled (Cy5, Cy3) samples attach to probes

• comparative genome hybridization (CGH): DNA samples

• gene expression: RNA samples

• relative intensity of hybridization can be measured

Cy5 Cy3

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Data flow

• biological data, DNA/RNA extraction, fluoresence dye labeling, hybridizationarray

• scanningimage• image processing: spot segmentationdatafile

• data preprocessing and normalization:• data analysis1: statistical tests to find differentially expressed

genes gene lists

• data analysis2: biological interpretations of results

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Image processing• segmentation: spot signals are extracted from

background• intensity information from both spot foreground

and background• other information like spot size and shape

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Image analysis results file

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Plotting data

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Logarithm of ratio

• log(Cy5/Cy3) = log(Cy5) – log(Cy3)

• log2(4/1) = 2• log2(2/1) = 1• log2(1/1) = 0• log2(1/2) = -1• log2(1/4) = -2

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Plotting data

• scatterplot• MA plot (Ratio vs Intensity)

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Normalization

• goal: to remove the effects of non-biological causes from data (dye-effect, hybridization, scanning, noise) and keep the biological information as well as possible

• normalization can be based on the behavior of the majority of the spots on the array, or small set of special control spots

• each normalization method is based on some assumption of the data

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Spot background subtraction

• how to know if spot signal is real and not just noise?• comparison against background signal• global versus local background• should background subtraction be used or not?

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Normalization• can be applied to both single channel and ratio data• mean

• variance

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Mean normalization

• global mean vs intensity dependent mean• Loess/Lowess normalization

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Print tip loess normalization

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Control spots (spike-in controls)

fold change up 3log2(3)=1.58

fold change up 10log2(10)=3.32

fold change down 10log2(1/10)=-3.32

fold change down 3log2(1/3)=-1.58

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What is the best normalization method?

• each method is based on some assumption each method can fail

• if utilizing the behavior of majority of the spots, array should represent all genes

• if utilizing control spots, check if they are reliable

• lots of methods have been introduced, lots of methods will be introduced…

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Finding differentially expressed genes

• Manually set fold change cutoff• Fold change cutoff based on data• Statistical test, p-value

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Limma package in R

• analysis of microarray data– data import– data plotting– data normalization– statistical tests differentially expressed genes

• online help and tutorial available> help(package=limma)

> library(limma)

> limmaUsersGuide()