Experimental design

In single-cell transcriptomics, careful experimental design is crucial to minimize batch effects and confounding factors that could lead to spurious clustering and mask the true biological signal. Ensuring an adequate number of samples and incorporating appropriate controls tailored to your research question is equally important to generate robust and interpretable data.

This guide provides key considerations for designing your single-cell experiment. We do not take responsibility for the completeness of the information provided. Always adapt your experimental design to the specifics of your study.

What is a batch effect?

A batch effect refers to systematic differences in your data caused by technical factors rather than biological variation. These effects can arise due to:

  • Differences in sample preparation protocols
  • Optimize your sample preparation protocol and maintain consistency! Always use the same reagents and equipment.
  • The person performing the sample preparation
  • Ideally, a single person should handle all samples to minimize variability.
  • The day of sample preparation
  • Process as many samples as possible per day to reduce variability across batches.

Some batch effects are unavoidable, such as those caused by processing samples on different days or involving multiple individuals in sample preparation. To mitigate their impact, it is essential to:

  • Record all batch-related variables (e.g. date, operator, reagent lot numbers).
  • Ensure a balanced experimental design, distributing conditions (e.g. treatment, genotype) and biological covariates (e.g. sex, age, post-mortem interval) evenly across batches.
  • Address batch effects during data analysis if necessary.

How to design your single-cell experiment:

  1. Define sample groups and controls
    • Determine the number of samples per group based on your research question.
    • Ensure appropriate controls (e.g., age-matched samples).
  2. Identify key biological covariates
    • Common covariates include sex, age, treatment, genotype, and post-mortem interval (for human samples).
  3. Plan sample preparation to minimize batch effects
    • Distribute covariates evenly across batches.
    • Avoid processing only control samples on one day or all younger samples first.
    • Create a sample processing schedule and document it for downstream analysis.

By carefully planning and recording your experiment, you can reduce batch effects and improve the reliability of your single-cell data.