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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. Two colour. Short oligo. cDNA. Long oligo. Genome wide. Custom.

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microarray basics

Microarray Basics

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

experimental design
Experimental design
  • What type of microarray
  • What overall design strategy
  • How many replicates
slide3

Type of Microarray

One colour

Two colour

Short oligo

cDNA

Long oligo

Genome wide

Custom

availability, cost, represented genes, need,

perceived accuracy/reproducibility

slide4

Cy3

Cy3

reference

reference

x40

healthy

healthy

healthy

healthy

healthy

healthy

x20

Cy5

Cy5

disease

disease

disease

disease

disease

disease

Cy3

x10

x10

Cy5

Cy3

x40

Cy5

Experimental Design Strategies

how many replicates
How many replicates?

True situation

Not diff expressed

Diff expressed

correct

Type 2 error

NS

(power)

Your call

Type 1 error

correct

S

(confidence)

Technical replicates do NOT count as different samples

in the power calculation

slide6

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
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
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?
slide9

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

checking rna quality
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
slide11

Provides an RIN

Provides a [ ]

Requires 1 µl of ~50ng/µl stock

rna amplification
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
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
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
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
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
rna quality issues
RNA quality issues
  • General low intensity
  • Consistent problems with one sample, regardless of fluor used
  • High level of background-

grainy over entire slide

experimental issues
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
slide21
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
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
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
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
slide25

Information about the experiment

Data presented as ratios

Raw quantitated data

locator and identifier columns
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
spot values
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
quality control measurements
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
data cleaning
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
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
on chip controls
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 controls1
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
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
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
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
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**

slide39

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

slide40

Linear regression of

Cy3 against Cy5

ma plots
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

slide42

Scatterplot of intensities

MA plot of same data

non linear normalization
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

slide44

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