Transcript E.coli mikrosirujen analyysi

cDNA microarrays
Panu Somervuo, March 19, 2007
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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
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Cy5
Cy3
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Data flow
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biological data, DNA/RNA extraction, fluoresence dye labeling,
hybridizationarray
scanningimage
image processing: spot segmentationdatafile
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data preprocessing and normalization:
data analysis1: statistical tests to find differentially expressed
genes  gene lists
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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)
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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
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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 10
log2(10)=3.32
fold change up 3
log2(3)=1.58
fold change down 3
log2(1/3)=-1.58
fold change down 10
log2(1/10)=-3.32
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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()
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