Stanley E. Lazic

Experimental Design for Laboratory Biologists (eBook, PDF)

Maximising Information and Improving Reproducibility

• Format: PDF

Produktbeschreibung

Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience. An accompanying website (https://stanlazic.github.io/EDLB.html) includes all R code, data sets, and the labstats R package. This is an ideal guide for anyone conducting lab-based biological research, from students to principle investigators working in either academia or industry.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 8. Dezember 2016
• Englisch
• ISBN-13: 9781316811030
• Artikelnr.: 53368870

Autorenporträt

Stanley E. Lazic holds a PhD in Neuroscience and a Masters degree in Computational Biology from the University of Cambridge and has conducted research at the University of Oxford, the University of Cambridge, and Harvard University, Massachusetts. He has written several papers on reproducible research and the design and analysis of biological experiments, and has published in Science and Nature. He is currently a Team Leader in Quantitative Biology (Statistics) at AstraZeneca.

Inhaltsangabe

1. Introduction: 1.1 What is reproducibility?
1.2 The psychology of scientific discovery
1.3 Are most published results wrong?
1.4 Frequentist statistical interference
1.5 Which statistics software to use?
2. Key ideas in experimental design: 2.1 Learning versus confirming experiments
2.2 The fundamental experimental design equation
2.3 Randomisation
2.4 Blocking
2.5 Blinding
2.6 Effect type: fixed versus random
2.7 Factor arrangement: crossed versus nested
2.8 Interactions between variables
2.9 Sampling
2.10 Use of controls
2.11 Front-aligned versus end-aligned designs
2.12 Heterogeneity and confounding
3. Replication (what is 'N'?): 3.1 Biological units
3.2 Experimental units
3.3 Observational units
3.4 Relationship between units
3.5 How is the experimental unit defined in other disciplines?
4. Analysis of common designs: 4.1 Preliminary concepts
4.2 Background to the designs
4.3 Completely randomised designs
4.4 Randomised block designs
4.5 Split-unit designs
4.6 Repeated measures designs
5. Planning for success: 5.1 Choosing a good outcome variable
5.2 Power analysis and sample size calculations
5.3 Optimal experimental designs (rules of thumb)
5.4 When to stop collecting data?
5.5 Putting it all together
5.6 How to get lucky
5.7 The statistical analysis plan
6. Exploratory data analysis: 6.1 Quality control checks
6.2 Preprocessing
6.3 Understanding the structure of the data
Appendix A. Introduction to R
Appendix B. Glossary.