Microarray Basics

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

Experimental design

• What type of microarray

• What overall design strategy

• How many replicates

One colour Two colour

cDNA Long oligo

Genome wide Custom

Short oligo

availability, cost, represented genes, need, perceived accuracy/reproducibility

Type of Microarray

reference

healthy disease

referenceCy3

Cy5x40

healthy

disease

Cy3

Cy5

x20

healthy

disease healthy

diseaseCy3

Cy5x10x10

healthy

healthy disease

diseaseCy3

Cy5x40

Experimental Design Strategies

How many replicates?

Your callNS

S

True situation

Not diff expressed Diff expressed

correct

correctType 1 error

Type 2 error

(confidence)

(power)

Technical replicates do NOT count as different samplesin 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

You have a 10,000 gene chip, and want to identify 95% of the genes that are 2 fold up or down regulated in samplesfollowing treatment. You will tolerate 1 false positive callout of the 10,000 genes tested. The coefficient of variabilityin your population is ~ 50%. You are doing a pairedanalysis.

Experimental design

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 largeamounts of high quality RNA

Animal tissues: some require harsh disruption treatments (such as soft tissues like kidney or liver) and some may requireaddition treatments (such as fatty tissues or fibrous tissues that may require more stringent lysis)

Blood: may be influence by anticoagulant in collection system, andalso 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

• 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 RINProvides 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

• 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 background-

grainy 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

• 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

• 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

Spot Values

Quality control measurements

• M/Y spot diameter

• N/Z spot area

• O/AA spot footprint

• P/ABspot circularity

• Q/AC spot uniformity

• R/AD background uniformity

• S/AEsignal to noise ratio

Data Cleaning

Are there flagged spots?

Are there areas of the images that you just wouldn’t trust?

Have the option of removing, recalculating, ignoring , flagging or resetting the results of these spots so that they don’t interferewith downstream analysis

Are there saturated spots?

-may see flags in last column- these are added byuser during quantitation

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 non-empty 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 lowerleft cornerVariability increases withintensity

Data are spread more evenlyVariability 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 arelatively large surface

Need to try to eliminate some of these potential sourcesof variation so that the variation that is left is more likelyto 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 normalizationto try to fit the data

Slope does not equal 1 means one channel responds more at higherintensityNon zero intercept means one channel is consistently brighter

Non straight line means non linearity in intensity responses of two channels

Linear regression ofCy3 against Cy5

MA plotsRegressing 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 seeingdeviations from a diagonal line than a horizontal line

MA plots get around both theseissues

Basically a rotation and rescaling of the data

M= log2R-log2G

A= (log2R + log2G)/2X axis

Y axis

Scatterplot of intensities

MA plot of same data

Non linear normalization

Normalization that takes into account intensity effects

User defines the size of bins used to calculate thebest fit line

Taken from Stekal (2003) Microarray Bioinformatics

Lowess or loess is the locally weighted polynomial regression

Adjusted values for the xaxis (average intensity foreach feature) calculated usingthe loess regression

Should now see the datacentred around 0 and straight across the horizontalaxis

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

• Perseus Missirlis

• Natasha Gallo

• Jim Gore

• Jennifer Kreiger

• Scott Davey