Transcript Microarray Basics - Queen's University

Microarray Basics
Part 1: Choosing a platform, setting
up, data preprocessing
Experimental design
• What type of microarray
• What overall design strategy
• How many replicates
Type of Microarray
One colour
Short oligo
Two colour
cDNA
Genome wide
Long oligo
Custom
availability, cost, represented genes, need,
perceived accuracy/reproducibility
Experimental Design Strategies
Cy3
reference
reference
Cy5
healthy
disease
Cy3
Cy5
healthy
disease
x40
Cy3
healthy
Cy5
disease
disease
x10
healthy
Cy3
disease
healthy
Cy5
healthy
disease
x40
x10
x20
How many replicates?
True situation
Not diff expressed
NS
correct
Diff expressed
Type 2 error
(power)
Your call
S
Type 1 error
(confidence)
correct
Technical replicates do NOT count as different samples
in the power calculation
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
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
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?
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 difficultextractions may also be highly viscous
Bacteria: may want to consider stabilization
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
Provides an RIN
Provides a [ ]
Requires 1 µl of ~50ng/µl stock
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
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
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
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
Slide issues- printing
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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
RNA quality issues
• General low intensity
• Consistent problems with one sample,
regardless of fluor used
• High level of backgroundgrainy over entire slide
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
• 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
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
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
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
Information about the experiment
Data presented as ratios
Raw quantitated data
Locator and identifier columns
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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
Spot Values
• I/U:
• J/V:
• K/W:
• L/X:
intensity of signal in ch1/ch2
intensity of background in ch1/ch2
std dev of intensity of signal in
ch1/ch2
std dev of background of signal in
ch 1/ch2
Quality control measurements
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M/Y
N/Z
O/AA
P/AB
Q/AC
R/AD
S/AE
spot diameter
spot area
spot footprint
spot circularity
spot uniformity
background uniformity
signal to noise ratio
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 wouldn’t
trust?
Are there saturated spots?
Have the option of removing, recalculating, ignoring , flagging or
resetting the results of these spots so that they don’t interfere
with downstream analysis
At this stage, may also want to background subtract the raw
intensities
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 nonempty spots printed with the same pin
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
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
Log transformation of data
Most data bunched in lower
left corner
Variability increases with
intensity
Data are spread more evenly
Variability is more even
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
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
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**
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
Linear regression of
Cy3 against Cy5
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
X axis
A= (log2R + log2G)/2
Y axis
M= log2R-log2G
Scatterplot of intensities
MA plot of same data
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
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
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
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
Acknowledgements
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Perseus Missirlis
Natasha Gallo
Jim Gore
Jennifer Kreiger
• Scott Davey