Fundamentals of experimental design for cDNA microarrays

Gary A. Churchill

http://www.jax.org/research/churchill

2004. 3. 20김하성

SUBJECT

• Fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis

OUTLINE

1. Source of variation in microarray experiments

2. Experimental units and treatments

3. Pairing samples for hybridizations

4. Printing the slides

5. Randomization

6. Analysis

7. Examples

mRNA

cDNA

DNA microarray

1. SOURCES OF VARIATION IN MICROARRAY EXPERIMENTS

• The correlation observation– A single microarray slide observe two times :

95%– Same sample divided and hybridized two

different microarray : 60%~80%– Different lab : lower

various source of variations

Avoid biological replication.

2. EXPERIMENTAL UNITS AND TREATMENTS #1

• Identifying the independent units in an experiment– Interest : Characterize that sample accurately -

> one sample, multiple slide -> independent replication

– Interest : a biological comparison at the whole organism -> one sample, multiple slide -> no longer independent replication.

2. EXPERIMENTAL UNITS AND TREATMENTS #2

2. EXPERIMENTAL UNITS AND TREATMENTS #3

A B

2 Treatments, 24 Observations, 8 experimental unit

A B

2 Treatments, 24 Observations, 2 experimental unit

• Degrees of freedom(df)– A simple way to assess the adequacy of a design– df = number of independent unit – number of distinct t

reatments– No df = no information of variance– 5 df or more = good shape.

• Pooling

2. EXPERIMENTAL UNITS AND TREATMENTS #4

3. PAIRING SAMPLES FOR HYBRIDIZATIONS #1

Direct Comparison of Two Treatments

a. A dye swapb. A repeated dye swapc. A replicated dye swapd. Simple loop design

3. PAIRING SAMPLES FOR HYBRIDIZATIONS #2Direct Comparison of Multiple Samples

3. PAIRING SAMPLES FOR HYBRIDIZATIONS #3Indirect Comparisons via a Reference Sample

a. The standard reference design use a single array to compare each test sample to the reference RNAb. A variation use a dye swap for each comparison.

subarray

Slide

gene

spot

pingroup

4. PRINTING THE SLIDES

- The arrangement of spots Raises design issues that can impact on normalization and analysis of microarray data

- Repeated spotting of the same clone increase precision of the measurements if the spot intensities are averaged and minimize problems ( scratches, dust, contaminate the surface of microarray slide)

- Repeated spot should be dispersed over the microarray surface to minimize correlations .

5. RANMOMIZAION #1

1) Randomization of treatment assignments and random sampling of populations form the physical basis for the validity of statistical test.

It is most crucial to apply randomization or random sampling at the stage of assigning treatments to the experimental unit for the validity of statistical test. Ex) injection of a drug(bias),

the sex and strain of a mouse(already attatched)

2) Randomization can be used at other statges in the microarray experiment to help avoid or minimize hidden biases;

Ex) dye assignmet first sample – Cy5, second sampe – Cy3 bias.

5. RANMOMIZAION #2

1) Randomizing the arrangement of spots on an array.

Fisher – regular arrangements cause potential biases.

Each slide in an experiment might have clones printed in a different arrangement. but impractical (printing device or logistics of tracking spot identities)

The possibility of position effects within the array is not farfetched, but it may be a reality that we simply have to accept with awareness.

6. ANALYSIS

1) Not touched on issues of analysis ( review by D.K. Slonim, p 502~508)

2) A well designed experiment will often suggest a suitable method of analysis.(kerr, churchil Analysis of variance for gene expression microarray data )

3) Recommend that analysis should be carried out in collaboration with a statistician until standards of microarray design and analysis evolve further

7. EXAMPLES #1 Mouse Mammary Tumor Survey #1

1 2 3

H eN

1 2 3

H eJ

1 2 3

B A L B

1 2 3

Y b R

• A survey of mouse mammary tumor samples(G.A. Churchill)

• Treatment factor is strain.

• Four levels strain and each strain represented by three independent tumor

• 8 df = 12(exp unit) – 4(treatment)

YBrYBrYBr

Hen

HeN

HeN

HeJ HeJ HeJ

BALB

BALB

BALB

REF

7. EXAMPLES #2Mouse Mammary Tumor Survey #2

• RNA from each tumor was compared directly to a reference sample using two arrays in a dye-swap arrangement

7. EXAMPLES #3Variation in Fundulus Species

1 2 3 4 5

Northern

1 2 3 4 5

Southern Grandis

1 2 3 4 5

• A study of variation in natual population of teleost fish.

• Treatment factor is population

• Three populations and five fish were sampled from each.

• 12 df = 15(exp unit) – 3(treatment)

N3

N5

N1

G2

N2

N4

S2

S1

G1

S5

S4

S3

G5

G4

G3

1,149 GenesEach arrow represents a

microarray with the arrowjoining the two sampleshybridize to the array.

7. EXAMPLES #4Geographic Variation in Fundulus

• The direct comparisons were arranged as loops

• Each sample was measured using four technical replicate.

• Dye assignments were balanced

7. EXAMPLES #5A Two Factor Design Sex x Diet

with 2x replication

• Gene expression in liver tissues of mice from Pera and DBA/I on low-fat and high-fat diets.

• Two independent pools and three mice providing 6df ( 12 pools – 6groups )

8. CONCLUSIONS

• Designing a microarray experiment – replication of biological samples

– technical replicates

– duplication of spotted clones

• Some important points to keep in mind – Use adequate biological replicaton

– Make direct comparisons between samples

– Use dye swapping or looping to balance dyes and samples

– Always keep the goals of the experiment in mind