Sample Size for Affymetrix Micrarray Experiments

Sample Size Selection for Microarray based Gene

Expression Studies

Gregory R. Warnes, Gregory R. Warnes, Pfizer Global R&DPfizer Global R&D

Fasheng LiFasheng LiSmith Hanley Consulting Smith Hanley Consulting

GroupGroup

Industry/FDA Statistics Workshop: September 18-19, 2003

Outline What is the context?What is the context? What is the problem?What is the problem? What are possible approaches?What are possible approaches? What approach was chosen and why?What approach was chosen and why? How was the approach implemented?How was the approach implemented? What do the results look like?What do the results look like? Future plans?Future plans? ReferencesReferences


What is Pfizer Global R&D? What do we do? What do we do? Lots!Lots!

Pharmaceutical research and development Associated basic science, medical, and technological research

How are we doing? How are we doing? Very WellVery Well 2003 R&D budget: $7.1 billion 33 major research projects across 10 major therapeutic

categories 12,000 employees 6 Major Research Sites

Groton/New London, CTGroton/New London, CT Ann Arbor, MichiganAnn Arbor, Michigan St. Louis, MissouriSt. Louis, Missouri

La Jolla, CaliforniaLa Jolla, California Sandwich, EnglandSandwich, England Nagoya, JapanNagoya, Japan


How are we using Gene Expression Technologies?

Determine regulatory and metabolic Determine regulatory and metabolic pathwayspathways Identify potential Identify potential biomarkersbiomarkers Identify potential Identify potential targetstargets Determine Determine mechanism of actionmechanism of action (desired and undesired) (desired and undesired) Evaluate / predict Evaluate / predict safetysafety Determine Determine mechanism of toxicitymechanism of toxicity


What is the problem? Gene expression assays areGene expression assays are expensive expensive

~ $2,000 per sample for Affymetrix experiments Good experimentalGood experimental design is important design is important

A A huge number of variableshuge number of variables measured on each measured on each experimental unitexperimental unit 9,300 variables the Affymetrix S98 Yeast Genechip™ 16,000 variables for Affymetrix RAE230a Rat Genechip™ 23,000 + 23,000 = 46,000 variables for the Affymetrix U133A

and U133B Human Genechips™ Sample size calculations are hardSample size calculations are hard


Standard sample size calculation

For a For a singlesingle outcome variable, given outcome variable, given simple designsimple design (e.g., two-sample t-test) (e.g., two-sample t-test) effect size effect size (ideally, minimum practical significance)(ideally, minimum practical significance) population variance population variance ²²,, significance levelsignificance level (probability of a false positive when (probability of a false positive when

no true effect)no true effect) powerpower (probability of a true positive given the defined (probability of a true positive given the defined

effect size)effect size)It is It is straightforward to computestraightforward to compute the required sample size the required sample size nn

(see e.g. Cochrain & Cox (1957))(see e.g. Cochrain & Cox (1957))


Gene expression sample size calculation

When there are When there are thousands of outcome variables thousands of outcome variables which are which are not independentnot independent, , many problemsmany problems arise: arise:

How to handle How to handle multiple comparisonmultiple comparison?? How to deal with How to deal with dependenciesdependencies?? One effect size or manyOne effect size or many?? One power or manyOne power or many?? Many variables, how to get a Many variables, how to get a single answersingle answer??


What are possible approaches?Two extremes:Two extremes:

Treat each variable (gene) as a separate and independent problem, Treat each variable (gene) as a separate and independent problem, then summarizethen summarize+ easy to set up, understand, explain + available data can be used- may not be sufficiently realistic, hence accuracy may suffer

Model the entire system, including realistic error structure and Model the entire system, including realistic error structure and interdependenciesinterdependencies+ may be more accurate (if model is good)- more initial work to set up / compute- may require substantial new data to be realistic- May be hard to understand, explain


What approach was chosen and why? We chose to treat each variable (gene) as a separate We chose to treat each variable (gene) as a separate

and independent problem, then summarizeand independent problem, then summarize Why?Why?

First approximations usually yield a useful information with minimal effort.

Answers were needed immediately. At best, results would only be used for general guidance A more realistic error model didn’t work:

We tried fitting the model from Zien, et al (2002), which requires high-dimensional numerical integration via MCMC or equivalent. However, the model appears to be non-identifiable.


How was the approach implemented? Compute variance of each geneCompute variance of each gene (variable) from existing studies (variable) from existing studies Assume a Assume a two sample t-test on log(expression)two sample t-test on log(expression) Bonferonni adjust significance value: Bonferonni adjust significance value: ii = = / #variables / #variables Generate plots of cumulative #genes :Generate plots of cumulative #genes :

1. Fixed I, , 1- vs. sample size (e.g. n=5/group,6/group,…)

2. Fixed I, , n vs. power (eg. 1-= 60%, 70%, 80%, …)

3. Fixed I, 1-, n vs. effect size (=1.5x, 2.0x, 2.5x, …) Run Run twicetwice: :

1. ‘candidate’ genes ( less stringent Bonf. Adj.)2. all genes

Implemented using Implemented using R [Ross & Ihaka, 1996] R [Ross & Ihaka, 1996] using theusing the power.t.test power.t.test function.function.


Standard Deviation (for data on the log scale)

Freq

uenc

y

0.0 0.5 1.0 1.5 2.0 2.5

050

100

150

200

Histogram of Standard Devations (Focus List)

What do the results look like? Standard Deviations: Focus Group


0 5 10 15 20

Sample Size (per group)

Pro

porti

on o

f Gen

es N

eedi

ng S

ampl

e S

ize

<= n

0%

=0 2

8%=2

00 5

6%=4

00 8

4%=6

00

30.42%=3

4

5.4%=39

5

22%=156

6

35%=251

8

48%=344

10

58%=412

20

78%=560

fold change=2alpha=0.05power=0.8# genes=716

Sample Size to Detect 2-Fold Change (Focus List Genes)

What do the results look like? Fixed I, , 1- vs. Sample Size: Focus Group


0.0 0.2 0.4 0.6 0.8 1.0

Power

Pro

porti

on o

f Gen

es w

ith P

ower

>=

x

0%

=0 2

8%=2

00 5

6%=4

00 8

4%=6

00

0.7

39%=277

0.8

35%=251

0.9

30%=215

n=6fold change=2alpha=0.05# genes=716

Power to Detect 2-Fold Change (Focus List Genes)

What do the results look like?Fixed I, , n vs. Power: Focus Group


2 4 6 8 10

Fold Change

Pro

porti

on o

f Gen

es w

ith P

ower

>=

80%

at F

old

Cha

nge=

delta

0%

=0 2

8%=2

00 5

6%=4

00 8

4%=6

00

1.5

8%=57

2

35%=251

2.5

47%=338

3

57%=407

4

66%=475

6

77%=549

10

86%=613

n=6alpha=0.05power=0.8# genes=716

Fold Change to Achieve 80% Power (Focus List)

What do the results look like?Fixed I, 1-, n vs. Fold Change: Focus

Group


Standard Deviation (for data on the log scale)

Freq

uenc

y

0.0 0.5 1.0 1.5 2.0 2.5 3.0

010

0020

0030

0040

0050

0060

00Histogram of Standard Deviations (All Genes)What do the results look like?

Standard Deviations: All Genes


5 10 15 20

Sample Size (per group)

Pro

porti

on o

f Gen

es N

eedi

ng S

ampl

e S

ize

<= n

0%

=0 3

2%=4

000 6

3%=8

000 9

5%=1

2000

41.1%=135

5

13%=1652

6

34%=4337

8

62%=7822

10

73%=9252

20

91%=11541

fold change=2alpha=0.05power=0.8# genes=12625

Sample Size to Detect 2-Fold Change (All Genes)

What do the results look like? Fixed I, , 1- vs. Sample Size: All Genes


0.0 0.2 0.4 0.6 0.8 1.0

Power

Pro

porti

on o

f Gen

es w

ith P

ower

>=

x

0%

=0 3

2%=4

000

63%

=800

0 95%

=120

00

0.7

41%=5116

0.8

34%=4337

0.9

27%=3427

n=6fold change=2alpha=0.05# genes=12625

Power to Detect 2-Fold Change (All Genes)

What do the results look like?Fixed I, , n vs. Power: All genes


2 4 6 8 10

Fold Change

Pro

porti

on o

f Gen

es w

ith P

ower

>=

80%

at F

old

Cha

nge=

delta

0%

=0 2

8%=2

00 5

6%=4

00 8

4%=6

00

1.5

8%=57

2

35%=251

2.5

47%=338

3

57%=407

4

66%=475

6

77%=549

10

86%=613

n=6alpha=0.05power=0.8# genes=716

Fold Change to Achieve 80% Power (Focus List)

What do the results look like?Fixed I, 1-, n vs. Fold Change: All Genes


Future plans?

1.1. AA web-applet web-applet backed by R to perform the calculations backed by R to perform the calculations


Future plans?

1.1. Provide a Provide a web-appletweb-applet backed by R to perform the backed by R to perform the calculationscalculations

2.2. Use a Use a library of gene variation information library of gene variation information in normal in normal samples, (structured by organism, Affymetrix chip type, samples, (structured by organism, Affymetrix chip type, cell type, normalization/scaling method)cell type, normalization/scaling method)

3.3. Extend to Extend to more complicated designsmore complicated designs (2-way ANOVA, (2-way ANOVA, Repeated measures, etc)Repeated measures, etc)

4.4. Other types of multiple comparison adjustments (Other types of multiple comparison adjustments (FDRFDR))5.5. Develop models that deal with correlations between Develop models that deal with correlations between

genes.genes.


References Two-sample t-test sample size:Two-sample t-test sample size:

Cochrain WG, Cox GM (1953). Experimental Designs (2nd Ed). 17-28. General sample size calculationsGeneral sample size calculations::

Chow SC, Liu JP (1998). Design and Analysis of Clinical Trials : Concept and Methodologies. Wiley-Interscience. Chapter 10, 424 – 482

Chow SC , Shao J, Wang H (2003). Sample Size Calculation in Clinical Research. Marcel Dekker [New, looks interesting]

Gene expression experiments sample size:Gene expression experiments sample size: Zien A, Fluck J, Zimmer R, Lengauer T (2002). Microarrays: How Many Do You Need?

RECOMB02, Meyers G, Hannenhalli S, Istrail S, Pevzner P, Waterman M, eds. 321-330. Statistical analysis softwareStatistical analysis software::

Ihaka R, Gentleman R, et al (2003). http://www.r-project.org [web site] Ross Ihaka and Robert Gentleman (1996). R: A Language for Data Analysis and Graphics,

Journal of Computational and Graphical Statistics, Vol 5, Number 3: 299-314. Web applet softwareWeb applet software::

Warnes GR, (2003). http://www.analytics.washington.edu/Zope/projects/RSessionDA/ [web site]

Me: Me: http://www.warnes.net

http://www.r-project.org/

http://www.analytics.washington.edu/Zope/projects/RSessionDA/

http://www.warnes.net/

Sample Size for Affymetrix Micrarray Experiments

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