Statistical experimental design principles for biological studies
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1 Statistical experimental design principles for biological studies Alexander (Alec) Zwart 10 July 2014 CSIRO DIGITAL PRODUCTIVITY AND SERVICES FLAGSHIP
2 An apology, and a caveat Apology: I am not a genetics/genomics expert! But I DO know a bit about design Caveat: Experimental design is a science! (as is statistical analysis ) Every experiment is different This talk is necessarily simplistic, and very incomplete Intended as a reminder of some concepts, some bits of advice, and a plea 2 Presentation title Presenter name
3 An experiment recap: Apply treatments (experimental conditions) or treatment combinations to subjects (or experimental units). Carefully control/remove other influences as much as possible. Observe one or more responses (observed or measured results) A way to infer cause => effect. Genotype/Species/Variety is a treatment? Yes... 3 Presentation title Presenter name
4 What is statistical experimental design concerned with? Everything! The entire process leading to the final dataset Recognising sources of variation (systematic, random) that influence your results and hence controlling for them How to ensure that we obtain data that provides honest and accurate answers to our questions 4 Presentation title Presenter name
5 Key point: Good statistics cannot rescue a bad dataset! And a bad dataset may not be able to answer your questions (honestly, that is) Do the experiment right => get a good dataset Consider design from the very beginning of planning the experiment 5 Presentation title Presenter name
6 Fundamentals Randomisation Blocking Replication 6 Presentation title Presenter name
7 Randomisation Allocate treatments to subjects randomly (ideally using a proper random number generator, not your own personal judgement) Also randomise: Spatial layout Order of processing Avoid systematic patterns (But: see Blocking) Protects against unforeseen biases 7 ED for Biology Alexander Zwart
8 Blocking Identify groupings that may effect your results Choice of operator/technician/machine Cage/Table/shelf/assay plate/batch Try to assign (ideally) one of each treatment to each group! (Randomised Block Design or RBD) (Example coming later ) 8 ED for Biology Alexander Zwart
9 Replication Sample size G*Power ( Russ Lenth s applets ( Simulation studies in the literature? (Google scholar) Biological/internal/technical replicates Don t confuse these Internal/technical replicates are not a substitute for biological replication. (more later ) 9 ED for Biology Alexander Zwart
10 12 mice, three treatments (Colour) Completely randomised design Balanced (equal info on each treatment, treatments compared with equal accuracy) 10 Presentation title Presenter name
11 But, the mice must be housed Here, treatments are confounded with cages Cannot separate Cage effects from Trt effects! 11 Presentation title Presenter name
12 Cages as Blocks 12 Presentation title Presenter name
13 Animals (especially) can pose a problem Grrr! Grrr! Grr Eeeek! 13 Presentation title Presenter name
14 Cages as Experimental Units / Subjects & Cage = Mouse 14 Presentation title Presenter name
15 (Aside env. trend across columns of cages? => Block by Columns! 15 Presentation title Presenter name
16 Not enough cages? A compromise: But, Cages are the Experimental units Only two replicates of each Trt!
17 More on replication: The replication used determines the population(s) being sampled......hence The replication used determines the conclusions that may be validly drawn from a statistical test 17 ED for Biology Alexander Zwart
18 Comparison between populations of plants Wild Type Mutant ED for Biology Alexander Zwart
19 Comparisons between two plants Wild Type Mutant ED for Biology Alexander Zwart
20 Recording Details of treatments and associated randomisation, blocking, types of replication should always be recorded Check what information is required for the analysis, or talk to the statistician about what is required. This is important meta data when distributing your data 20 ED for Biology Alexander Zwart
21 More on Blocking One of each treatment per block is not always feasible blocks may be too small Incomplete blocks Think about: Balance treatments occur together in the same blocks the same number of times. Linkage common treatments in different blocks provide links between blocks ensure you have plenty of linkage. 21 ED for Biology Alexander Zwart
22 Balanced Incomplete Block Design (BIBD) - Trts occur together the same number of times - Plenty of treatment-linkages between blocks
23 Notes on incomplete blocks BIBDs are only availble for certain combinations of parameters. However, the general principles are still relevant Unbalanced incomplete blocks Note that teasing apart the treatment effects from block effects in unbalanced situations is dependent on the capabilities of the analysis method Check on said capabilities, consider simpler experiment if necessary 23 ED for Biology Alexander Zwart
24 Treatment structure Sir Ronald Fisher => factorial treatment structure (3x4 factorial) 5 = Number of replicates of each treatment 24 ED for Biology Alexander Zwart
25 Factorial treatment structure Separate main effects and interactions Can include more factors (in theory) Unbalanced numbers of treatments usually OK: 25 ED for Biology Alexander Zwart
26 But beware missing treatment combinations The more that entire treatments are missing from the factorial, the harder it is to tease apart main effects and interactions and the more confounded different treatment effects become. 26 ED for Biology Alexander Zwart
27 Don t get too ambitious! Two way factorials are much easier to interpret than three way, four way etc. The higher the factorial structure, the more likely the structure will be incomplete (missing values, treatment combos which don t make sense, treatment combos you aren t interested in and don t include). Statisticians and their tools can t work miracles! Simpler is better Talk to a statistician! 27 ED for Biology Alexander Zwart
28 Plea: If you don t already know how to design and conduct good experiments then involve a statistician in the early stages of planning your experiment! And note the statistician may need time 28 Presentation title Presenter name
29 Sir Ronald Fisher, the father of modern statistics To consult the statistician after an experiment is finished is often merely to ask them to conduct a post mortem examination. They can perhaps say what the experiment died of. (R. Fisher) 29 Presentation title Presenter name
30 Thank you CSIRO DIGITAL PRODUCTIVITY AND SERVICES FLAGSHIP
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