Checklist for Experimental Design

October 25, 2021 at 9:42 am | | everyday science, scientific integrity

One of the worst feelings as a scientist is to realize that you performed n-1 of the controls you needed to really answer the question you’re trying to solve.

My #1 top recommendation when starting a new set of experiments is to write down a step-by-step plan. Include what controls you plan to do and how many replicates. Exact details like concentrations are less important than the overarching experimental plan. Ask for feedback from your PI, but also from a collaborator or labmate.

Here are some things I consider when starting an experiment. Feel free to leave comments about things I’ve missed.

Randomization and Independence

  • Consider what “independent” means in the context of your experiment.
  • Calculate p-value using independent samples (biological replicates), not the total number of cells, unless you use advanced hierarchical analyses (see: previous blog post and Lord et al.: https://doi.org/10.1083/jcb.202001064).
  • Pairing or “blocking” samples (e.g. splitting one aliquot into treatment and control) helps reduce confounding parameters, such as passage number, location in incubator, cell confluence, etc.

Power and Statistics

  • Statistical power is to the ability to distinguish small but real differences between conditions/populations.
  • Increasing the number of independent experimental rounds (AKA “biological replicates”) typically has a much larger influence on power than the cells or number of measurements per sample (see: Blainey et al.: https://doi.org/10.1038/nmeth.3091).
  • If the power of an assay is known, you can calculate the number of samples required to be confident you will be able to observe an effect.
  • Consider preregistering
    • Planning the experimental and analysis design before starting the experiment substantially reduces the chances false positives.
    • Although formal preregistration is not typically required for cell biology studies, simply writing the plan down for yourself in your notebook is far better than winging it as you go.
    • Plan the number of biological replicates before running statistical analysis. If you instead check if a signal is “significant” between rounds of experiment and stop when p < 0.05, you’re all but guaranteed to find a false result.
    • Similarly, don’t try several tests until you stumble upon one that gives a “significant” p-value.

Controls

  • Sledgehammer controls
    • These controls are virtually guaranteed to give you zero or full signal, and are a nice simple test of the system.
    • Examples include wild type cells, treating with DMSO, imaging autofluorescence of cells not expressing GFP, etc.
  • Subtle controls
    • These controls are more subtle than the strong controls, and might reveal some unexpected failures.
    • Examples include: using secondary antibody only, checking for bleed-through and crosstalk between fluorescence channels, and using scrambled siRNA.
  • Positive & negative controls
    • The “assay window” is a measure of the range between the maximum expected signal (positive control) and the baseline (negative control).
    • A quantitative measure of the assay window could be a standardized effect size, like Cohen’s d, calculated with multiple positive and negative controls.
    • In practice, few cell biologist perform multiple control runs before an experiment. So a qualitative estimate of the assay window should be considered using the expected signal and expected variability sample to sample. In other words, consider carefully if an experiment can possibly work
  • Internal controls
    • “Internal” controls are cells within the same sample that randomly receive treatment or control. For example, during a transient transfection, only a portion of the cells may actually end up expressing, while those that aren’t can act as a negative control.
    • Because cells with the same sample experience the same perturbations (such as position in incubator, passage number, media age) except for the treatment of interest, internal controls can remove many spurious variables and make analysis more straightforward.

Bias

  • Blinding acquisition
    • Often as simple as having a labmate put tape over labels on your samples and label them with a dummy index. Confirm that your coworker actually writes down the key, so later you can decode the dummy index back to the true sample information.
    • In cases where true blinding is impractical, the selection of cells to image/collect should be randomized (e.g. set random coordinates for the microscope stage) or otherwise designed to avoid bias (e.g. selecting cells using transmitted light or DAPI).
  • Blinding analysis
    • Ideally, image analysis would be done entirely by algorithms and computers, but often the most practical and effective approach is old-fashioned human eye.
    • Ensuring your manual analysis isn’t biased is usually as simple as scrambling filenames. For microscopy data, Steve Royle‘s macro, which works well: https://github.com/quantixed/imagej-macros#blind-analysis
    • I would highly recommend copying all the data to a new folder before you perform any filename changes. Then test the program forward and backwards to confirm everything works as expected. Maybe perform analysis in batches, so in case something goes awry, you don’t lose all that work.

Resources

Great primer on experimental design and analysis, especially for the cell biologist or microscopist: Stephen Royle, “The Digital Cell: Cell Biology as a Data Science” https://cshlpress.com/default.tpl?action=full&–eqskudatarq=1282

Advanced, detailed (but easily digestible) book on experimental design and statistics: Stanley Lazic, “Experimental Design for Laboratory Biologists” https://stanlazic.github.io/EDLB.html

I like this very useful and easy-to-follow stats book: Whitlock & Schluter, “The Analysis of Biological Data” https://whitlockschluter.zoology.ubc.ca

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