Scientific Inquiry - Experimental design session 2025

This page contains links to material for the experimental design session of the FMI Scientific Inquiry course 2024.

• Slides Charlotte
• Slides Michael (PDF)
• Exercises on confounding (for self-study)
• Notes on design matrices (for self-study)

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Resources

Nature Methods ‘Statistics for Biologists’ collection:

• N Altman & M Krzywinski: Sources of variation. Nat Methods 12:5-6 (2015)
• M Krzywinski & N Altman: Designing comparative experiments. Nat Methods 11:597-598 (2014)
• M Krzywinski & N Altman: Analysis of variance and blocking. Nat Methods 11:699-700 (2014)
• P Blainey, M Krzywinski & N Altman: Replication. Nat Methods 11:879-880 (2014)
• M Krzywinski, N Altman & P Blainey: Nested designs Nat Methods 11:977-978 (2014)
• M Krzywinski & N Altman: Two-factor designs. Nat Methods 11:1187-1188 (2014)

Other relevant material:

• Slide deck from Susan Holmes and Wolfgang Huber on Experimental Design

• R package containing a shiny app to explore confounding and different experimental designs

• A blog post about interpreting p-value histograms

• MR Wagner & M Kleiner: How thoughtful experimental design can empower biologists in the omics era

• M Baker: 1,500 scientists lift the lid on reproducibility. Nature 533:452–454 (2016)

• LB Sheiner: Learning vs confirming in clinical drug development. Clinical Pharmacology and Therapeutics 61(3):275-291 (1997)

• PL Auer & RW Doerge: Statistical design and analysis of RNA sequencing data. Genetics 185(2):405-416 (2010)

• SE Lazic: Experimental design for laboratory biologists. Cambridge University Press (2016)

• SE Lazic et al: What exactly is ‘N’ in cell culture and animal experiments? PLoS Biol 16(4): e2005282 (2018)

• P Norvig: Warning signs in experimental design and interpretation

• JT Leek et al: Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics 11:733–739 (2010)

• MJ Larsen et al: Microarray-Based RNA Profiling of Breast Cancer: Batch Effect Removal Improves Cross-Platform Consistency. Biomed Res Int 2014: 651751 (2014)

• S Lin et al: Comparison of the transcriptional landscapes between human and mouse tissues. PNAS 111(48):17224-17229 (2014)

• Y Gilad & O Mizrahi-Man: A reanalysis of mouse ENCODE comparative gene expression data. F1000Research 4:121 (2015)

• J Hu et al: The importance of experimental design in proteomic mass spectrometry experiments: Some cautionary tales. Briefings in Functional Genomics 3(4):322–331 (2005)

• B Klaus: Statistical relevance - relevant statistics, part I. EMBO Journal 34:2727-2730 (2015)

Archive

2024