Gene Expression Microarrays
Microarray Normalization
Xiaole Shirley Liu
STAT115, STAT215, BIO512, BIST298
Announcement: you can register for
ANY of the above 4 courses now
Microarrays
• Grow cells at certain condition, collect
mRNA population, and label them
• Microarray has high density sequence
specific probes with known location for
each gene/RNA
• Sample hybridized to microarray probes by
DNA (A-T, G-C) base pairing, wash nonspecific binding
• Measure sample mRNA value by checking
labeled signals at each probe location
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Spotted cDNA Arrays
• Pat Brown Lab, Stanford
University
• Robotic spotting of cDNA
(mRNA converted back to
DNA, no introns)
• Several thousand probes /
array
• One long probe per gene
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Spotted cDNA Arrays
• Competing hybridization
– Control
– Treatment
• Detection
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Green: high control
Red: high treatment
Yellow: equally high
Black: equally low
Why Competing Hybridization?
• DNA concentration in probes not the
same, probes not spotted evenly
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Oligonucleotide Arrays
• Some Design Considerations
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More or fewer probes / array?
Long or short oligos?
Same or different probe lengths?
How many probes / gene?
How are probes placed on the array?
One- or two-color assay
Affymetrix Oligo Arrays
• GeneChip® by Affymetrix
• Parallel synthesis of
oligonucleotide probes (25mer) on a slide using
photolithographic methods
• Millions of probes /
microarray
• Multiple probes per gene
• One-color arrays
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Affymetrix GeneChip Probes
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Labeled Samples Hybridize to DNA
Probes on GeneChip
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Shining Laser Light Causes
Tagged Fragments to Glow
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Perfect Match (PM) vs MisMatch (MM)
(control for cross hybridization)
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NimbleGen
Oligo Arrays
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Agilent Oligo Arrays
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Why do we bother learning about
microarrays now?
• RNA-seq is probably preferred in new
expression experiments
• The amount of useful public data
• The data analysis techniques
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Public Microarray Resources
• GEO: Gene Expression Omnibus, a NCBI
repository for gene expression and hybridization
data, growing quickly.
• TCGA: The Cancer Genome Atlas
– http://www.cbioportal.org/public-portal/
– https://cghub.ucsc.edu/
– http://www.broadinstitute.org/cancer/cga/
• Oncomine: Cancer Microarray Database
– Published cancer related microarrays
– Raw data all processed, nice interface
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Affymetrix Microarray Imagine Analysis
• Gridding: based on spike-in DNA
• Affymetrix GeneChip Operating System
(GCOS)
– cel file
X
701
702
Y
523
523
MEAN
311.0
48.0
STDV
76.5
10.5
NPIXELS
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– cdf file
• Which probe at (X,Y) corresponds to which probe
sequence and targeted transcript
• MM probes always (X,Y+1) PM
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Replicates
• Always preferred
• Biological replicates:
– Different animals, tissues, etc
• Technical replicates:
– Repeated measures of the same sample
• In between:
– Same cell line grown on different days
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Normalization
• Try to preserve biological variation and
minimize experimental variation, so
different experiments can be compared
• Assumption: most genes / probes don’t
change between two conditions
• Normalization can have larger effect on
analysis than downstream steps (e.g. group
comparisons)
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Median Scaling
• Linear scaling
array1
array1
– Ensure the different arrays have the same
median value and same dynamic range
– X' = (X – c1) * c2
array2
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array2
LOESS
• LOcally WEighted Scatterplot Smoothing,
more general form is LOESS
• Fit a smooth curve
– Use robust local linear fits
– Effectively applies different scaling factors at
different intensity levels
– Y = f(X)
– Transform X to X' = f(X)
– Y and X' are comparable
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Quantile Normalization
• Bolstad et al Bioinformatics 2003
– Currently considered the best normalization method
– Assume most of the probes/genes don’t change between samples
• Calculate mean for each quantile and reassign each probe
by the quantile mean
• No experiment retain value, but all experiments have
exact same distribution
Experiments
Probes
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Mean
How to Visualize Microarray
Normalization?
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Dilution Series
• RNA sample in 5 different concentrations
• 5 replicates scanned on 5 different scanners
• Before and after quantile normalization
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MvA Plot
log2R vs log2G
Values should be
on diagonal
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M=log2R- log2G
A=(log2R+log2G)/2
Values should scatter
around 0
Before Normalization
• Pairwise MA plot for 5 arrays, probe (PM)
M  log 2 ( PM i / PM j )
A  log 2 PM i  PM j
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After Normalization
• Pairwise MA plot for 5 arrays, probe (PM)
M  log 2 ( PM i / PM j )
A  log 2 PM i  PM j
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When Might qnorm Fail?
• Loven et al, Cell 2012
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Summary
• Microarrays: Different oligo arrays
• Array normalization: Loess, qnorm
– Assumptions
• Normalization visualization: MA plots
• We will cover batch effect removal after
clustering analysis…
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