Microarray Basics

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Microarray Basics

• Part 1 Choosing a platform, setting up, data
  preprocessing

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Experimental design

• What type of microarray
• What overall design strategy
• How many replicates

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Type of Microarray
One colour
Two colour
cDNA
Long oligo
Short oligo
Genome wide
Custom
availability, cost, represented genes, need,
perceived accuracy/reproducibility
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Experimental Design Strategies
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How many replicates?
True situation
Not diff expressed
Diff expressed
correct
Type 2 error
NS
(power)
Your call
correct
Type 1 error
S
(confidence)
Technical replicates do NOT count as different
samples in the power calculation
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Power analysis requires decisions about
Difference in mean that you are trying to detect
The std dev of the population variability
Power you are trying to achieve
Significance level that you are trying to achieve
Experimental design
You have a 10,000 gene chip, and want to identify
95 of the genes that are 2 fold up or down
regulated in samples following treatment. You
will tolerate 1 false positive call out of the
10,000 genes tested. The coefficient of
variability in your population is 50. You are
doing a paired analysis.
One can conclude that you will need 22 patients
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Technical replicates

• Most publications recommend at least 3 if that is
  possible
• These are considered to be replicates at the
  level of the experimental platform
• Beware of doing 2 now and hoping to add one more
  later
• In downstream analysis, generally suggested to
  use the average of technical replicates- these
  are not different samples for analysis

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RNA required to get started

• Source of both experimental and reference RNA
• Will need about 10-20ug of total RNA from each
  source for each experiment or chip
• This RNA needs to be of high quality
• How do you check quality?

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Common sources of RNA
Cultured animal cells generally easy to disrupt
and get large amounts of high quality RNA
Animal tissues some require harsh disruption
treatments (such as soft tissues like kidney or
liver) and some may require addition treatments
(such as fatty tissues or fibrous tissues that
may require more stringent lysis)
Blood may be influence by anticoagulant in
collection system, and also seems to contain
enzyme inhibitors
Plant material some metabolites make
purification difficult- extractions may also be
highly viscous
Bacteria may want to consider stabilization
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Checking RNA quality

• Conventional methods include agarose gel
  electrophoresis to look for evidence of
  degradation
• Spectrophotometric readings to give an idea of
  purity
• Bioanalyzer to provide scan- integrity and
  quantity measurements

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Provides an RIN
Provides a
Requires 1 µl of 50ng/µl stock
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RNA amplification

• When quantity of RNA is limited, may have to
  consider amplification
• Several strategies, but need to decide up front
  if you want sense or antisense amplified material

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What do you get back after an experiment?

• TIFF images- one image for each fluor used in the
  experiment- same chip scanned twice (or more
  times if multiple scans were done to compensate
  for intensity)
• Spreadsheet of quantitated data

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TIFF images

• Generally named as bar code_fluor_PMT
  setting_laser setting
• These settings will not necessarily be the same
  for your two scans from the same chip- they are
  manipulated to try to produce scans of even
  intensity from the two fluors
• The final image should have only a few white
  spots over the whole array- these represent
  saturated spots

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How can you tell anything about the quality of
your data?

• Easiest way to start is to look at your TIFF
  images
• Look for blank areas on the slide
• Look for areas where one fluor consistently is
  brighter than the other
• Look for gradients of intensity
• Differentiate between artifacts introduced by
  slide quality and those by RNA quality and those
  by experimental procedure

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Slide issues- printing

• Presence of donuts
• Smeared spots
• Scratches on surface of slide
• Non circular spots
• Spots off the grid
• No signals in areas
• Consistent problems with the same area of each
  subarray

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RNA quality issues

• General low intensity
• Consistent problems with one sample, regardless
  of fluor used
• High level of background-
• grainy over entire slide

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Experimental issues

• One fluor consistently not giving good signal
  regardless of RNA sample labelled
• High areas of local background
• not covering entire slide
• Obvious intensity gradients
• Bubbles over surface of chip

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• After looking at your images you should have a
  sense of whether or not these data are likely to
  be clean and high enough quality to warrant
  proceeding
• If not you need to try to determine where the
  problem originates

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Image processing

• Choice of methods for quantitating image
• Fixed circle
• Good for arrays with regular sizes of spots
• Variable circle
• Better for arrays with irregular sizes
• Histogram
• Best for arrays with irregular sizes and shapes

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Data quantitation

• The images are quantitated, generating a lengthy
  spreadsheet
• This is done in the facility using QuantArray,
  but can be done using other freeware (Scanalzye)
  or commercial software
• The output can generally be opened in Excel for
  first pass manipulation of data

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QuantArray output

• QA generates a series of columns that many people
  find confusing
• In general, it provides the data in two ways on a
  single sheet- the first method is showing one
  channel as a proportion of the other, the second
  method provides absolute pixel counts for each
  channel

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Information about the experiment
Data presented as ratios
Raw quantitated data
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Locator and identifier columns

• A unique number assigned to that spot
• B Row of subgrid
• C Column of subgrid
• D Row of spot within subgrid
• E Column of spot within subgrid
• F Gene identification
• G x coordinate of each spot
• H y coordinate of each spot

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Spot Values

• I/U intensity of signal in ch1/ch2
• J/V intensity of background in ch1/ch2
• K/W std dev of intensity of signal in ch1/ch2
• L/X std dev of background of signal in ch
  1/ch2

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Quality control measurements

• M/Y spot diameter
• N/Z spot area
• O/AA spot footprint
• P/AB spot circularity
• Q/AC spot uniformity
• R/AD background uniformity
• S/AE signal to noise ratio

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Data Cleaning
Are there flagged spots?
-may see flags in last column- these are added
by user during quantitation
Are there areas of the images that you just
wouldnt trust?
Are there saturated spots?
Have the option of removing, recalculating,
ignoring , flagging or resetting the results of
these spots so that they dont interfere with
downstream analysis
At this stage, may also want to background
subtract the raw intensities
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On chip controls and how they behave

• Blank spots generally 3XSSC (print buffer)
• Expect no signal- can use the average or median
  intensity of these spots as the lower cutoff for
  what represents a real signal
• However not all empty spots are the same on some
  chips
• Possibility that there is carryover from
  non-empty spots printed with the same pin

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On chip controls

• Multiple spots of the same gene
• In general if it is exactly the same sequence,
  can assess the variability of these spots to
  assess artifacts of geography on the chip
• If it is not the same sequence, less
  straightforward

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On chip controls

• Housekeeping genes if you can identify a set of
  genes that should remain at constant expression,
  can use these to standardize the two channels
• to correctly identify such genes is difficult
• May also have exogenous controls that can be
  added, but must identify these prior to
  hybridizing the slides

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Log transformation of data
Most data bunched in lower left
corner Variability increases with intensity
Data are spread more evenly Variability is more
even
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Within array normalization
In two colour arrays, are measuring two different
samples, labelled in two different reactions with
two different fluors and measured using two
different lasers at two different
wavelengths In addition, dealing with the
distribution of spots across a relatively large
surface
Need to try to eliminate some of these potential
sources of variation so that the variation that
is left is more likely to be due to biological
effects
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Dye Bias

• The two dyes incorporate differently into DNA of
  different abundance
• The two dyes may have different emission
  responses to the laser at different abundances
• The two dye emissions may be measured by the PMT
  differently at different intensities
• The intensities of the dyes may vary over the
  surface of the slide, but not in synch, as the
  focus of each laser is separate

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Correcting for dye bias

• Global normalization using median or mean
• Linear regression of Cy3 against Cy5
• Linear regression of the log ratio against the
  average intensity (MA plots)
• Non linear regression of the log ratio against
  the average intensity (loess)
• assumption that most genes are not
  differentially expressed

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Simple global normalization to try to fit the data
Slope does not equal 1 means one channel responds
more at higher intensity
Non zero intercept means one channel is
consistently brighter
Non straight line means non linearity in
intensity responses of two channels
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Linear regression of Cy3 against Cy5
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MA plots
Regressing one channel against the other has the
disadvantage of treating the two sets of signals
separately
Also suggested that the human eye has a harder
time seeing deviations from a diagonal line than
a horizontal line
MA plots get around both these issues
Basically a rotation and rescaling of the data
A (log2R log2G)/2
X axis
M log2R-log2G
Y axis
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Scatterplot of intensities
MA plot of same data
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Non linear normalization
Normalization that takes into account intensity
effects
Lowess or loess is the locally weighted
polynomial regression
User defines the size of bins used to calculate
the best fit line
Taken from Stekal (2003) Microarray Bioinformatics
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Adjusted values for the x axis (average intensity
for each feature) calculated using the loess
regression
Should now see the data centred around 0 and
straight across the horizontal axis
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Spatial defects over the slide

• In some cases, you may notice a spatial bias of
  the two channels
• May be a result of the slide not lying completely
  flat in the scanner
• This will not be corrected by the methods
  discussed before

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Regressions for spatial bias

• Carry out normal loess regression but treat each
  subgrid as an entire array (block by block loess)
• Corrects best for artifacts introduced by the
  pins, as opposed to artifacts of regions of the
  slide
• Because each subgrid has relatively few spots,
  risk having a subgrid where a substantial
  proportion of spots are really differentially
  expressed- you will lose data if you apply a
  loess regression to that block
• May also perform a 2-D loess- plot log ratio for
  each feature against its x and y coordinates and
  perform regression

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Acknowledgements

• Perseus Missirlis
• Natasha Gallo
• Jim Gore
• Jennifer Kreiger
• Scott Davey