Download 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
Panu Somervuo, March 19, 2007
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
•
data analysis2: biological interpretations of results
Panu Somervuo, March 19, 2007
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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
Panu Somervuo, March 19, 2007
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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?
Panu Somervuo, March 19, 2007
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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
Panu Somervuo, March 19, 2007
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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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