Statistical Issues in the Design of Microarray Experiments

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Statistical Issues in the Design of Statistical Issues in the Design of Microarray ExperimentsMicroarray Experiments

Lara LusaLara LusaU.O. Statistica Medica e BiometriaU.O. Statistica Medica e Biometria

Istituto Nazionale per lo Studio e la Cura dei Tumori, MilanoIstituto Nazionale per lo Studio e la Cura dei Tumori, Milano

NETTAB 2003NETTAB 2003

Bologna, 28th November 2003Bologna, 28th November 2003

Outline

• Biostatistics and microarrays

• Study objectives

• Design of microarray experiments

• A case study: a designed experiment

Biostatistics and microarrays

• Microarray research: unique challenge for interdisciplinary collaboration

• Can biostatisticians be useful in microarray research?

• Are available software tools a valid substitution for collaboration with biostatisticians?

What can biostatisticians do?

• Active collaboration with researchers from biomedical and bioinformatics fields– to develop and critically evaluate methods for

• design of microarray experiments

• analysis of data

– to perform data-analysis– to develop software tools and train biomedical

researchers to use them

Italian inter-university research group

• Statistical issues in design and analysis of microarray data

• MIUR grant 2003-2005– Firenze (Annibale Biggeri)– Milano (Giuseppe Gallus)– Padova (Monica Chiogna)– Torino (Mauro Gasparini)– Udine(Corrado Lagazio)

Collaborations

•Milano •Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano (statisticians, biologists, molecular oncologists)• IFOM, Milano (biologists, bioinformatics)• Biometric Research Branch, NCI, Bethesda (statisticians)•“Edo Tempia” Foundation, Biella• Bioconductor poject (software development)

Study objectives

• Class comparison (supervised)– establish differences in gene expression between

predetermined classes

• Class prediction (supervised)– prediction of phenotype using gene expression data

• Class discovery (unsupervised)– discover groups of samples or genes with similar

expression

Design of microarray experiments

• Design of arrays

• Allocation of samples– Replication– Labeling of samples (cDNA)

• reference design

• balanced block design

• loop design

Levels of replication

• Biological replicates– multiple samples from different populations

• Technical replicates– multiple samples from the same subject– multiple samples from the same mRNA– multiple clones or probes of the same gene on

the array

How many replicates?• Biological replicates essential to make inference

about population• Technical replicates useful for quality control

and for increasing precision• How to determine sample size?

– Problem-dependent– simple methods available for class comparison

problems – not yet clear what to use for class discovery

Common pitfalls in microarray experiments

• Too little or no replication• Use of replication at the wrong level• Experiments with cell lines: assuming no

variability among cell lines of the same type• Inappropriate use of pooling

– ok: use of multiple independent pools– but: is it useful? – individual information lost

Case study: a designed experiment

• Biological aim: assess the effect of Toremifen on MCF-7 breast cancer cell line, in terms of gene expression

A1 A2 A3

Control

B1 B2 B3

Control Control Treatment Treatment Treatment

Affymetrix

POOL POOL

CDNA Affymetrix CDNA Affymetrix

Week 1

A1 A2 A3

Control

B1 B2 B3

Control Control Treatment Treatment Treatment

POOL POOL

CDNA Affymetrix CDNA Affymetrix

Week 2 and 3

Statistical aims

• comparison of microarray platforms (cDNA vs Affymetrix)

• hybridization of individual samples vs pools

• variability of cell lines

• robustness of commonly used statistical methods

Data available (so far)

• Hybridizations from Affymetrix HGU133 Chips

• Summary measure of intensities: MAS5 (Affymetrix, 2002)

• most commonly used, but other possibilities available

– Robust Multichip Analysis (Irizarry et al., 2002)– Model-Based Expression Index (Li and Wong,

2001) (at least 16 chips!)

Brief summary of data• HGU133A:

– chipA : 22.283 probe sets– chipB : 22.645 probe sets

• Present – chipA: 48.5%– chipB: 38.2%

• pm<mm– chipA: 27%– chipB: 31%

Methods for exploring reproducibility among arrays

• Pearson’s coefficient of correlation (common, but wrong!)

• Coefficient of variation

• Distribution of differences of intensities

• Altman and Bland’s plot (MA plot)

Class comparison• Identification of differentially expressed genes

between treated and not treated cell lines

• t-tests (adjusting for multiple comparisons)– all arrays– only pooled arrays– only individual arrays

• ANOVA (linear) model – estimation of treatment effect, adjusting for pool effect

and week effect

Some results...

• Pooled variance t-test on whole data– treated versus controls:

• chipA– 1948 p<0.001 (356 p<0.001 and abs(FC)>2)

– 240 with Bonferroni correction

• chipB– 743 p<0.001 (143 p<0.001 and abs(FC)>2)

– 76 with Bonferroni correction

Some results...

• Pooled variance t-test on pooled data– treated versus controls:

• chipA– 204 p<0.001

» 189/(204, 1948) common to overall analysis

– 82 p<0.001 and abs(FC)>2

• chipB– 80 p<0.001

– 37 p<0.001 and abs(FC)>2

Some results...

• Pooled variance t-test on “individual” data– treated versus controls:

• chipA– 669 p<0.001

– 226 p<0.001 and abs(FC)>2

• chipB– 245 p<0.001

» 196(245, 743) common to overall analysis

– 80 p<0.001 and abs(FC)>2

Some results...

• Pooled variance ANOVA results– treated versus controls:

• chipA– 1913 p<0.001

– 343 p<0.001 and abs(FC)>2

• chipB– 245 p<0.001

» 196(245, 743) common to overall analysis

– 80 p<0.001 and abs(FC)>2

Conclusions

• ?????????

• So far no evidence for the usefulness of pooling data from cell lines… no evidence of decreased variability

• … but need to further investigate the differences in the “individual” versus “pooled” results

• Need for a plan of biological (quantitative) validations of expression measures

Statistical Issues in the Design of Microarray Experiments

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