1) Experimental Design is difficult -

1. NUGO_metabolomics_2010 Biostatistics • 1) Experimental Design is difficult - • Clinical power calculations to decide a replication strategy to address a new biological problem are not really possible in inductive (hypothesis-generating) experiments. • Many analytical experiments can be ‘archaeological’- decisions on replication and class structures have been made previously and often design was not tailored for a metabolomics study. • Good starting point – Before talking to a statistician be prepared to answer some basic questions. • - What is the question you are trying to address? • Screening (Inductive) • Modelling (partly inductive at training stage) • Estimation (Hypothesis driven from now) • Validation

2. - Can you be clear about where variance originates? - What is the extent and quality of meta-data? - If pilot study data available then can you confirm the classes are MORE or LESS discrete (e.g. use clustering software) Develop a Dialogue between Statistics experts and Biologists and Analytical Chemists Can Ca

3. 2) Once samples are available then analytical chemists benefit from input from statistics experts to design injection order, technical reps etc. 3) In general people were happy with both classification and feature selection tools for screening and modelling – caveats: …….having tools that incorporate a permutation facility is one of the more important aspects of any statistical ‘package’. …. Having ideas of expectations for ‘goodness’ of class separation is valuable (e.g. Eigenvalues, AUC, margins). …. Perhaps having fress access in R based software 4) Existing procedures for estimation and validation were already available. These may require different numbers of replicates and a further experiment – sometime this is tough for biologists asking for funding who need a more’ hybrid’ approach to design.