Some views on microarray experimental design Rainer Breitling Molecular Plant Science Group & Bioinformatics Research Centre University of Glasgow, Scotland, UK

Personal Background University of Glasgow, Scotland, UK Molecular Plant Sciences Group Bioinformatics Research Centre Functional Genomics Facility

Some common questions in microarray experimental design How many arrays will I need? Should I pool my samples? Which arrays should I choose? Which samples should I put together on one array?

Why are microarrays special? produce large amounts of data instantaneously can look for unexpected effects are still quite expensive  almost never repeated  careful design necessary before you start

How many replicates? as many as possible Statistics says: The more replicates, the better your estimate of expression (that’s an asymptotic process, so if you add at least a few replicates, the effect will be really strong)

How many replicates? α significance level (probability of detecting FP) 1-β power to detect differences (probability of detecting TP) σ standard deviation of the log-ratios δ detectable difference between class mean log-ratios z percentile of standard normal distribution  n required number of arrays (reference design)

How many replicates? Five Experience shows: For most common experiments you get a reasonable list of differentially expressed genes with 5 replicates

How many replicates? Three One to convince yourself, one to convince your boss, one just in case...

How many replicates? It depends on –the quality of the sample –the magnitude of the expected effect –the experimental design –the method of analysis

The quality of the sample smaller samples (single cells) are more noisy than large samples (tissue homogenates) cell cultures are less noisy than patient biopsies sample pooling can decrease noise – if individual variation is not of interest

The magnitude of the effect Microarrays are very sensitive To keep effects small: –use early time points, gentle stimuli –never compare dogs and donuts if you get a list of 2000 genes that are significantly changed, your experiment failed!

The magnitude of the effect some problematic cases –stably transfected cell lines (are they still the same cells?) –knock-out organisms (even the same tissue can be a different) –local changes may be diluted  cell isolation will increase noise

The experimental design Three major options: –reference design (flexible) –balanced block design (efficient) –loop design (elegant)

The experimental design loop designs can save samples......but they can cause interpretation nightmares in less simple cases (use for large studies, if you have a full-time statistician in the team) AB CD A BCD R RRR

The method of analysis Golub et al. (1999) data set 38 leukemia patient bone marrow samples, hybridized individually to Affymetrix microarrays Differential expression between two leukemia types was examined, using random subsets of the complete dataset

The method of analysis 0h9.5h11.5h13.5h15.5h18.5h20.5h purine base metabolism tricarboxylic acid cycle heat shock protein activity tricarboxylic acid cycle cell wall (sensu Fungi) heat shock protein activity respiratory chain complex II (sensu Eukarya) tricarboxylic acid cycle heat shock protein activity spermine transporter activity response to stress oxidative phosphorylation, succinate to ubiquinone glycogen metabolism respiratory chain complex II (sensu Eukarya) polyamine transport spermine transporter activity succinate dehydrogenase (ubiquinone) activity response to stress oxidative phosphorylation, succinate to ubiquinone glycogen (starch) synthase activity heat shock protein activity glycogen (starch) synthase activity succinate dehydrogenase (ubiquinone) activity polyamine transport glycogen (starch) synthase activity cytochrome c oxidase activity glutamate biosynthesis fructose transporter activity vacuolar protein catabolism respiratory chain complex IV (sensu Eukarya) glyoxylate cycle mannose transporter activity response to stress respiratory chain complex II (sensu Eukarya) respiratory chain complex III (sensu Eukarya) vacuolar protein catabolism cytochrome c oxidase activity oxidative phosphorylation, succinate to ubiquinone aerobic respiration hexose transport respiratory chain complex IV (sensu Eukarya) succinate dehydrogenase (ubiquinone) activity cytochrome c oxidase activity iterative GroupAnalysis (iGA)

glyoxylate cycle citrate (TCA) cycle oxidative phosphorylation (complex V) respiratory chain complex III respiratory chain complex II Graph-based iterative GroupAnalysis (GiGA)

What is a good replicate? The experiment your competitor at the other side of the globe would do to see if your results are reproducible  Vary “all” parameters – challenge your results  Prepare new samples, from new cultures, using new buffers and new graduate students  Remember to produce matched controls

What is a “bad” replicate? technical replicates (i.e. hybridizing the same sample repeatedly) dye-swapping experiments (usually gene- specific dye bias is not a big issue, and dye balancing is more efficient anyway) pooled samples, hybridized repeatedly the same preparation, only labelled twice

Should samples be pooled? most samples are already pooled – they come from multiple cells pool to increase amount of mRNA, but only as much as necessary prepare independent pools to assess variation problems: bias, “contamination”, outliers, information loss...

Which arrays are the best? Standard arrays compare and exchange data easily Whole-genome arrays detect unexpected effects, increase confidence Single-color arrays (Affymetrix GeneChip) for more complex comparisons Annotated arrays

Further reading Dobbin, Shih & Simon (2003) J. Natl. Cancer Inst. 95: Yang & Speed (2002) Nature Rev. Genet. 3: 579. Breitling (2004)

Contact Rainer Breitling Bioinformatics Research Centre Davidson Building A416